* [36] L. Cardamone, D. Loiacono, P. L. Lanzi, and A. P. Bardelli, “Searching for the optimal racing line using genetic algorithms,” in _Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games_. IEEE, Aug. 2010. [Online]. Available: https://doi.org/10.1109/itw.2010.5593330
  * [37] J. V. Carrau, A. Liniger, X. Zhang, and J. Lygeros, “Efficient implementation of randomized MPC for miniature race cars,” in _2016 European Control Conference (ECC)_. IEEE, Jun. 2016\. [Online]. Available: https://doi.org/10.1109/ecc.2016.7810413
  * [38] C. Chatzikomis, A. Sorniotti, P. Gruber, M. Zanchetta, D. Willans, and B. Balcombe, “Comparison of path tracking and torque-vectoring controllers for autonomous electric vehicles,” _IEEE Transactions on Intelligent Vehicles_ , vol. 3, no. 4, pp. 559–570, Dec. 2018. [Online]. Available: https://doi.org/10.1109/tiv.2018.2874529
  * [39] T. Chen, Z. Li, Y. He, Z. Xu, Z. Yan, and H. Li, “From perception to control: an autonomous driving system for a formula student driverless car,” 2019.
  * [40] E. Chisari, A. Liniger, A. Rupenyan, L. V. Gool, and J. Lygeros, “Learning from simulation, racing in reality,” 2021. [Online]. Available: arXiv:2011.13332
  * [41] F. Christ, A. Wischnewski, A. Heilmeier, and B. Lohmann, “Time-optimal trajectory planning for a race car considering variable tyre-road friction coefficients,” _Vehicle System Dynamics_ , vol. 59, no. 4, pp. 588–612, Dec. 2019. [Online]. Available: https://doi.org/10.1080/00423114.2019.1704804
  * [42] J. Culley, S. Garlick, E. G. Esteller, P. Georgiev, I. Fursa, I. V. Sluis, P. Ball, and A. Bradley, “System design for a driverless autonomous racing vehicle,” in _2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)_. IEEE, Jul. 2020. [Online]. Available: https://doi.org/10.1109/csndsp49049.2020.9249626
  * [43] N. D. Bianco, E. Bertolazzi, F. Biral, and M. Massaro, “Comparison of direct and indirect methods for minimum lap time optimal control problems,” _Vehicle System Dynamics_ , vol. 57, no. 5, pp. 665–696, Jun. 2018. [Online]. Available: https://doi.org/10.1080/00423114.2018.1480048
  * [44] T. de Bruin, J. Kober, K. Tuyls, and R. Babuska, “Integrating state representation learning into deep reinforcement learning,” _IEEE Robotics and Automation Letters_ , vol. 3, no. 3, pp. 1394–1401, Jul. 2018. [Online]. Available: https://doi.org/10.1109/lra.2018.2800101
  * [45] N. D. Rita, A. Aimar, and T. Delbruck, “CNN-based object detection on low precision hardware: Racing car case study,” in _2019 IEEE Intelligent Vehicles Symposium (IV)_. IEEE, Jun. 2019. [Online]. Available: https://doi.org/10.1109/ivs.2019.8814001
  * [46] A. Dhall, D. Dai, and L. V. Gool, “Real-time 3d traffic cone detection for autonomous driving,” in _2019 IEEE Intelligent Vehicles Symposium (IV)_. IEEE, Jun. 2019. [Online]. Available: https://doi.org/10.1109/ivs.2019.8814089
  * [47] D. Dodel, M. Schötz, and N. Vödisch, “Fsoco: The formula student objects in context dataset,” 2021.
  * [48] T. H. Drage, J. Kalinowski, and T. Bräunl, “Development of an autonomous formula SAE car with laser scanner and GPS,” _IFAC Proceedings Volumes_ , vol. 47, no. 3, pp. 2652–2657, 2014. [Online]. Available: https://doi.org/10.3182/20140824-6-za-1003.01386
  * [49] P. Drews, G. Williams, B. Goldfain, E. A. Theodorou, and J. M. Rehg, “Aggressive deep driving: Model predictive control with a cnn cost model,” 2017\.
  * [50] ——, “Vision-based high-speed driving with a deep dynamic observer,” _IEEE Robotics and Automation Letters_ , vol. 4, no. 2, pp. 1564–1571, Apr. 2019. [Online]. Available: https://doi.org/10.1109/lra.2019.2896449
  * [51] S. Eken, M. Şara, Y. Satılmış, M. Karslı, M. F. Tufan, H. Menhour, and A. Sayar, “A reproducible educational plan to teach mini autonomous race car programming,” _The International Journal of Electrical Engineering & Education_, vol. 57, no. 4, pp. 340–360, Feb. 2020\. [Online]. Available: https://doi.org/10.1177/0020720920907879
  * [52] B. Evans, H. A. Engelbrecht, and H. W. Jordaan, “From navigation to racing: Reward signal design for autonomous racing,” 2021.
  * [53] B. Evans, H. W. Jordaan, and H. A. Engelbrecht, “Autonomous obstacle avoidance by learning policies for reference modification,” 2021.
  * [54] D. Falanga, S. Kim, and D. Scaramuzza, “How fast is too fast? the role of perception latency in high-speed sense and avoid,” _IEEE Robotics and Automation Letters_ , vol. 4, no. 2, pp. 1884–1891, Apr. 2019. [Online]. Available: https://doi.org/10.1109/lra.2019.2898117
  * [55] S. Feraco, S. Luciani, A. Bonfitto, N. Amati, and A. Tonoli, “A local trajectory planning and control method for autonomous vehicles based on the RRT algorithm,” in _2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)_. IEEE, Nov. 2020. [Online]. Available: https://doi.org/10.23919/aeitautomotive50086.2020.9307439
  * [56] A. Foris, N. Wagener, B. Boots, and A. Mazumdar, “Exploiting singular configurations for controllable, low-power friction enhancement on unmanned ground vehicles,” _IEEE Robotics and Automation Letters_ , vol. 5, no. 2, pp. 3556–3563, Apr. 2020. [Online]. Available: https://doi.org/10.1109/lra.2020.2977266
  * [57] M. Fu, J. Ni, X. Li, and J. Hu, “Path tracking for autonomous race car based on g-g diagram,” _International Journal of Automotive Technology_ , vol. 19, no. 4, pp. 659–668, Jun. 2018. [Online]. Available: https://doi.org/10.1007/s12239-018-0063-7
  * [58] F. Fuchs, Y. Song, E. Kaufmann, D. Scaramuzza, and P. Durr, “Super-human performance in gran turismo sport using deep reinforcement learning,” _IEEE Robotics and Automation Letters_ , vol. 6, no. 3, pp. 4257–4264, Jul. 2021. [Online]. Available: https://doi.org/10.1109/lra.2021.3064284
  * [59] N. Funk, N. Alatur, R. Deuber, F. Gonon, N. Messikommer, J. Nubert, M. Patriarca, S. Schaefer, D. Scotoni, N. Bünger, R. Dube, R. Khanna, M. Pfeiffer, E. Wilhelm, and R. Siegwart, “Autonomous electric race car design,” 2017.
  * [60] J. Funke, P. Theodosis, R. Hindiyeh, G. Stanek, K. Kritatakirana, C. Gerdes, D. Langer, M. Hernandez, B. Muller-Bessler, and B. Huhnke, “Up to the limits: Autonomous audi TTS,” in _2012 IEEE Intelligent Vehicles Symposium_. IEEE, Jun. 2012. [Online]. Available: https://doi.org/10.1109/ivs.2012.6232212
  * [61] J. Funke, M. Brown, S. M. Erlien, and J. C. Gerdes, “Collision avoidance and stabilization for autonomous vehicles in emergency scenarios,” _IEEE Transactions on Control Systems Technology_ , vol. 25, no. 4, pp. 1204–1216, Jul. 2017. [Online]. Available: https://doi.org/10.1109/tcst.2016.2599783
  * [62] M. S. Gandhi, B. Vlahov, J. Gibson, G. Williams, and E. A. Theodorou, “Robust model predictive path integral control: Analysis and performance guarantees,” _IEEE Robotics and Automation Letters_ , vol. 6, no. 2, pp. 1423–1430, Apr. 2021. [Online]. Available: https://doi.org/10.1109/lra.2021.3057563
  * [63] J. Goh and J. C. Gerdes, “Simultaneous stabilization and tracking of basic automobile drifting trajectories,” in _2016 IEEE Intelligent Vehicles Symposium (IV)_. IEEE, Jun. 2016. [Online]. Available: https://doi.org/10.1109/ivs.2016.7535448
  * [64] J. Goh, T. Goel, and J. C. Gerdes, “Speed control for robust path-tracking for automated vehicles at the tire-road friction limit,” in _AVEC 18 -14th International Symposium on Advanced Vehicle Control_ , 2018. [Online]. Available: https://ddl.stanford.edu/sites/g/files/sbiybj9456/f/marty_avec2018_fullpaper.pdf
  * [65] ——, “Toward automated vehicle control beyond the stability limits: Drifting along a general path,” _Journal of Dynamic Systems, Measurement, and Control_ , vol. 142, no. 2, Nov. 2019. [Online]. Available: https://doi.org/10.1115/1.4045320
  * [66] B. Goldfain, P. Drews, C. You, M. Barulic, O. Velev, P. Tsiotras, and J. M. Rehg, “AutoRally: An open platform for aggressive autonomous driving,” _IEEE Control Systems_ , vol. 39, no. 1, pp. 26–55, Feb. 2019. [Online]. Available: https://doi.org/10.1109/mcs.2018.2876958
  * [67] N. Gosala, A. Buhler, M. Prajapat, C. Ehmke, M. Gupta, R. Sivanesan, A. Gawel, M. Pfeiffer, M. Burki, I. Sa, R. Dube, and R. Siegwart, “Redundant perception and state estimation for reliable autonomous racing,” in _2019 International Conference on Robotics and Automation (ICRA)_. IEEE, May 2019. [Online]. Available: https://doi.org/10.1109/icra.2019.8794155
  * [68] A. Gotlib, K. Lukojc, and M. Szczygielski, “Localization-based software architecture for 1:10 scale autonomous car,” in _2019 International Interdisciplinary PhD Workshop (IIPhDW)_. IEEE, May 2019. [Online]. Available: https://doi.org/10.1109/iiphdw.2019.8755418
  * [69] F. Gottmann, H. Wind, and O. Sawodny, “On the influence of rear axle steering and modeling depth on a model based racing line generation for autonomous racing,” in _2018 IEEE Conference on Control Technology and Applications (CCTA)_. IEEE, Aug. 2018\. [Online]. Available: https://doi.org/10.1109/ccta.2018.8511508
  * [70] K. Guckiran and B. Bolat, “Autonomous car racing in simulation environment using deep reinforcement learning,” in _2019 Innovations in Intelligent Systems and Applications Conference (ASYU)_. IEEE, Oct. 2019. [Online]. Available: https://doi.org/10.1109/asyu48272.2019.8946332
  * [71] I. Gundlach and U. Konigorski, “Modellbasierte online-trajektorienplanung für zeitoptimale rennlinien,” _at - Automatisierungstechnik_ , vol. 67, no. 9, pp. 799–813, Sep. 2019. [Online]. Available: https://doi.org/10.1515/auto-2019-0032
  * [72] P. K. Gundu, K. G. Vamvoudakis, and R. M. Gerdes, “An intermittent learning algorithm for high-speed autonomous driving in unknown environments,” in _2019 American Control Conference (ACC)_. IEEE, Jul. 2019. [Online]. Available: https://doi.org/10.23919/acc.2019.8814462
  * [73] G. Rong, B. H. Shin, H. Tabatabaee, Q. Lu, S. Lemke, M. Možeiko, E. Boise, G. Uhm, M. Gerow, S. Mehta, E. Agafonov, T. H. Kim, E. Sterner, K. Ushiroda, M. Reyes, D. Zelenkovsky, and S. Kim, “SVL Simulator: A High Fidelity Simulator for Autonomous Driving,” _arXiv e-prints_ , p. arXiv:2005.03778, May 2020\.
  * [74] K. Hart, C. Montella, G. Petitpas, D. Schweisinger, A. Shariati, B. Sourbeer, T. Trephan, and J. Spletzer, “RoSCAR,” in _Proceedings of the 2014 workshop on Mobile augmented reality and robotic technology-based systems - MARS '14_. ACM Press, 2014. [Online]. Available: https://doi.org/10.1145/2609829.2609837
  * [75] A. Heilmeier, M. Graf, and M. Lienkamp, “A race simulation for strategy decisions in circuit motorsports,” in _2018 21st International Conference on Intelligent Transportation Systems (ITSC)_. IEEE, Nov. 2018. [Online]. Available: https://doi.org/10.1109/itsc.2018.8570012
  * [76] A. Heilmeier, A. Wischnewski, L. Hermansdorfer, J. Betz, M. Lienkamp, and B. Lohmann, “Minimum curvature trajectory planning and control for an autonomous race car,” _Vehicle System Dynamics_ , vol. 58, no. 10, pp. 1497–1527, Jun. 2019. [Online]. Available: https://doi.org/10.1080/00423114.2019.1631455
  * [77] A. Heilmeier, M. Geisslinger, and J. Betz, “A quasi-steady-state lap time simulation for electrified race cars,” in _2019 Fourteenth International Conference on Ecological Vehicles and Renewable Energies (EVER)_. IEEE, May 2019. [Online]. Available: https://doi.org/10.1109/ever.2019.8813646
  * [78] J. Herman, J. Francis, S. Ganju, B. Chen, A. Koul, A. Gupta, A. Skabelkin, I. Zhukov, M. Kumskoy, and E. Nyberg, “Learn-to-race: A multimodal control environment for autonomous racing,” 2021.
  * [79] L. Hermansdorfer, J. Betz, and M. Lienkamp, “A concept for estimation and prediction of the tire-road friction potential for an autonomous racecar,” in _2019 IEEE Intelligent Transportation Systems Conference (ITSC)_. IEEE, Oct. 2019. [Online]. Available: https://doi.org/10.1109/itsc.2019.8917024
  * [80] ——, “Benchmarking of a software stack for autonomous racing against a professional human race driver,” in _2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER)_. IEEE, Sep. 2020. [Online]. Available: https://doi.org/10.1109/ever48776.2020.9242926
  * [81] L. Hermansdorfer, R. Trauth, J. Betz, and M. Lienkamp, “End-to-end neural network for vehicle dynamics modeling,” in _2020 6th IEEE Congress on Information Science and Technology (CiSt)_. IEEE, Jun. 2020. [Online]. Available: https://doi.org/10.1109/cist49399.2021.9357196
  * [82] T. Herrmann, F. Christ, J. Betz, and M. Lienkamp, “Energy management strategy for an autonomous electric racecar using optimal control,” in _2019 IEEE Intelligent Transportation Systems Conference (ITSC)_. IEEE, Oct. 2019. [Online]. Available: https://doi.org/10.1109/itsc.2019.8917154
  * [83] T. Herrmann, F. Passigato, J. Betz, and M. Lienkamp, “Minimum race-time planning-strategy for an autonomous electric racecar,” in _2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)_. IEEE, Sep. 2020. [Online]. Available: https://doi.org/10.1109/itsc45102.2020.9294681
  * [84] T. Herrmann, A. Wischnewski, L. Hermansdorfer, J. Betz, and M. Lienkamp, “Real-time adaptive velocity optimization for autonomous electric cars at the limits of handling,” _IEEE Transactions on Intelligent Vehicles_ , pp. 1–1, 2020. [Online]. Available: https://doi.org/10.1109/tiv.2020.3047858
  * [85] T. Herrmann, F. Sauerbeck, M. Bayerlein, J. Betz, and M. Lienkamp, “Optimization-based real-time-capable energy strategy for autonomous electric race cars [ saeindextestsamplev2 2022-01-17t10:06:32.798262z],” _SAE International Journal of Connected and Automated Vehicles_ , vol. 5, no. 1, jan 2022.
  * [86] L. Hewing, A. Liniger, and M. N. Zeilinger, “Cautious NMPC with gaussian process dynamics for autonomous miniature race cars,” in _2018 European Control Conference (ECC)_. IEEE, Jun. 2018. [Online]. Available: https://doi.org/10.23919/ecc.2018.8550162
  * [87] R. Y. Hindiyeh and J. C. Gerdes, “A controller framework for autonomous drifting: Design, stability, and experimental validation,” _Journal of Dynamic Systems, Measurement, and Control_ , vol. 136, no. 5, Jul. 2014. [Online]. Available: https://doi.org/10.1115/1.4027471
  * [88] I. Georgiev, C. Chatzikomis, T. Völkl, J. Smith, and M. N. Mistry, “Iterative semi-parametric dynamics model learning for autonomous racing,” _CoRR_ , vol. abs/2011.08750, 2020. [Online]. Available: https://arxiv.org/abs/2011.08750
  * [89] R. Ivanov, T. J. Carpenter, J. Weimer, R. Alur, G. J. Pappas, and I. Lee, “Case study,” in _Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control_. ACM, Apr. 2020. [Online]. Available: https://doi.org/10.1145/3365365.3382216
  * [90] A. Jain, M. O’Kelly, P. Chaudhari, and M. Morari, “Bayesrace: Learning to race autonomously using prior experience,” 2020.
  * [91] M. Jaritz, R. de Charette, M. Toromanoff, E. Perot, and F. Nashashibi, “End-to-end race driving with deep reinforcement learning,” in _2018 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, May 2018. [Online]. Available: https://doi.org/10.1109/icra.2018.8460934
  * [92] J. H. Jeon, R. V. Cowlagi, S. C. Peters, S. Karaman, E. Frazzoli, P. Tsiotras, and K. Iagnemma, “Optimal motion planning with the half-car dynamical model for autonomous high-speed driving,” in _2013 American Control Conference_. IEEE, Jun. 2013. [Online]. Available: https://doi.org/10.1109/acc.2013.6579835
  * [93] X. Ji, X. He, C. Lv, Y. Liu, and J. Wu, “Adaptive-neural-network-based robust lateral motion control for autonomous vehicle at driving limits,” _Control Engineering Practice_ , vol. 76, pp. 41–53, Jul. 2018. [Online]. Available: https://doi.org/10.1016/j.conengprac.2018.04.007
  * [94] E. Joa, H. Cha, Y. Hyun, Y. Koh, K. Yi, and J. Park, “A new control approach for automated drifting in consideration of the driving characteristics of an expert human driver,” _Control Engineering Practice_ , vol. 96, p. 104293, Mar. 2020. [Online]. Available: https://doi.org/10.1016/j.conengprac.2019.104293
  * [95] A. Joglekar, B. Deshpande, M. Basuthakur, and V. N. Krovi, “Fusing offline and online trajectory optimization techniques for goal-to-goal navigation of a scaled autonomous vehicle,” in _SAE Technical Paper Series_. SAE International, Apr. 2021. [Online]. Available: https://doi.org/10.4271/2021-01-0097
  * [96] J. Kabzan, L. Hewing, A. Liniger, and M. N. Zeilinger, “Learning-based model predictive control for autonomous racing,” _IEEE Robotics and Automation Letters_ , vol. 4, no. 4, pp. 3363–3370, Oct. 2019. [Online]. Available: https://doi.org/10.1109/lra.2019.2926677
  * [97] J. Kabzan, M. I. Valls, V. J. F. Reijgwart, H. F. C. Hendrikx, C. Ehmke, M. Prajapat, A. Bühler, N. Gosala, M. Gupta, R. Sivanesan, A. Dhall, E. Chisari, N. Karnchanachari, S. Brits, M. Dangel, I. Sa, R. Dubé, A. Gawel, M. Pfeiffer, A. Liniger, J. Lygeros, and R. Siegwart, “AMZ driverless: The full autonomous racing system,” _Journal of Field Robotics_ , Aug. 2020. [Online]. Available: https://doi.org/10.1002/rob.21977
  * [98] N. R. Kapania and J. C. Gerdes, “Design of a feedback-feedforward steering controller for accurate path tracking and stability at the limits of handling,” _Vehicle System Dynamics_ , vol. 53, no. 12, pp. 1687–1704, Jun. 2015. [Online]. Available: https://doi.org/10.1080/00423114.2015.1055279
  * [99] ——, “Path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control,” in _2015 American Control Conference (ACC)_. IEEE, Jul. 2015. [Online]. Available: https://doi.org/10.1109/acc.2015.7171151
  * [100] N. R. Kapania, J. Subosits, and J. C. Gerdes, “A sequential two-step algorithm for fast generation of vehicle racing trajectories,” _Journal of Dynamic Systems, Measurement, and Control_ , vol. 138, no. 9, Jun. 2016. [Online]. Available: https://doi.org/10.1115/1.4033311
  * [101] N. R. Kapania and J. C. Gerdes, “Learning at the racetrack: Data-driven methods to improve racing performance over multiple laps,” _IEEE Transactions on Vehicular Technology_ , vol. 69, no. 8, pp. 8232–8242, Aug. 2020\. [Online]. Available: https://doi.org/10.1109/tvt.2020.2998065
  * [102] S. Karaman, A. Anders, M. Boulet, J. Connor, K. Gregson, W. Guerra, O. Guldner, M. Mohamoud, B. Plancher, R. Shin, and J. Vivilecchia, “Project-based, collaborative, algorithmic robotics for high school students: Programming self-driving race cars at MIT,” in _2017 IEEE Integrated STEM Education Conference (ISEC)_. IEEE, 2017. [Online]. Available: https://doi.org/10.1109/isecon.2017.7910242
  * [103] J. C. Kegelman, L. K. Harbott, and J. C. Gerdes, “Insights into vehicle trajectories at the handling limits: analysing open data from race car drivers,” _Vehicle System Dynamics_ , vol. 55, no. 2, pp. 191–207, Nov. 2016\. [Online]. Available: https://doi.org/10.1080/00423114.2016.1249893
  * [104] D. P. Kelly and R. S. Sharp, “Time-optimal control of the race car: a numerical method to emulate the ideal driver,” _Vehicle System Dynamics_ , vol. 48, no. 12, pp. 1461–1474, Aug. 2010. [Online]. Available: https://doi.org/10.1080/00423110903514236
  * [105] T. Kessler, K. Esterle, and A. Knoll, “Linear differential games for cooperative behavior planning of autonomous vehicles using mixed-integer programming,” in _2020 59th IEEE Conference on Decision and Control (CDC)_. IEEE, Dec. 2020. [Online]. Available: https://doi.org/10.1109/cdc42340.2020.9304495
  * [106] D. Kloeser, T. Schoels, T. Sartor, A. Zanelli, G. Prison, and M. Diehl, “NMPC for racing using a singularity-free path-parametric model with obstacle avoidance,” _IFAC-PapersOnLine_ , vol. 53, no. 2, pp. 14 324–14 329, 2020. [Online]. Available: https://doi.org/10.1016/j.ifacol.2020.12.1376
  * [107] B. Korkmaz, U. B. Etlik, A. Beke, and T. Kumbasar, “Fuzzy logic based self-driving racing car control system,” in _2018 6th International Conference on Control Engineering & Information Technology (CEIT)_. IEEE, Oct. 2018. [Online]. Available: https://doi.org/10.1109/ceit.2018.8751900
  * [108] K. Kritayakirana and J. C. Gerdes, “Autonomous cornering at the limits: Maximizing a “g-g” diagram by using feedforward trail-braking and throttle-on-exit,” _IFAC Proceedings Volumes_ , vol. 43, no. 7, pp. 548–553, Jul. 2010. [Online]. Available: https://doi.org/10.3182/20100712-3-de-2013.00060
  * [109] ——, “Using the centre of percussion to design a steering controller for an autonomous race car,” _Vehicle System Dynamics_ , vol. 50, no. sup1, pp. 33–51, Jan. 2012. [Online]. Available: https://doi.org/10.1080/00423114.2012.672842
  * [110] ——, “Autonomous vehicle control at the limits of handling,” _International Journal of Vehicle Autonomous Systems_ , vol. 10, no. 4, p. 271, 2012. [Online]. Available: https://doi.org/10.1504/ijvas.2012.051270
  * [111] P. W. Kuhn, “Methodology for the numerical calculation of racing lines and the virtual assessment of driving behavior for training circuits for the automobile industry,” _Transportation Research Procedia_ , vol. 25, pp. 1416–1429, 2017. [Online]. Available: https://doi.org/10.1016/j.trpro.2017.05.167
  * [112] V. A. Laurense, J. Y. Goh, and J. C. Gerdes, “Path-tracking for autonomous vehicles at the limit of friction,” in _2017 American Control Conference (ACC)_. IEEE, May 2017. [Online]. Available: https://doi.org/10.23919/acc.2017.7963824
  * [113] V. Laurense and C. Gerdes, “Speed control for robust path-tracking for automated vehicles at the tire-road friction limit,” in _AVEC 18 -14th International Symposium on Advanced Vehicle Control_ , 2018. [Online]. Available: https://ddl.stanford.edu/publications/speed-control-robust-path-tracking-automated-vehicles-tire-road/-friction-limit
  * [114] K. Lee, G. N. An, V. Zakharov, and E. A. Theodorou, “Perceptual attention-based predictive control,” 2019.
  * [115] K. Lee, Z. Wang, B. I. Vlahov, H. K. Brar, and E. A. Theodorou, “Ensemble bayesian decision making with redundant deep perceptual control policies,” 2020\.
  * [116] N. L. Large, F. Bieder, and M. Lauer, “Comparison of different SLAM approaches for a driverless race car,” _tm - Technisches Messen_ , vol. 88, no. 4, pp. 227–236, Mar. 2021. [Online]. Available: https://doi.org/10.1515/teme-2021-0004
  * [117] A. Liniger, A. Domahidi, and M. Morari, “Optimization-based autonomous racing of 1:43 scale RC cars,” _Optimal Control Applications and Methods_ , vol. 36, no. 5, pp. 628–647, Jul. 2014. [Online]. Available: https://doi.org/10.1002/oca.2123
  * [118] A. Liniger and J. Lygeros, “A viability approach for fast recursive feasible finite horizon path planning of autonomous RC cars,” in _Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control_. ACM, Apr. 2015. [Online]. Available: https://doi.org/10.1145/2728606.2728620
  * [119] A. Liniger, X. Zhang, P. Aeschbach, A. Georghiou, and J. Lygeros, “Racing miniature cars: Enhancing performance using stochastic MPC and disturbance feedback,” in _2017 American Control Conference (ACC)_. IEEE, May 2017. [Online]. Available: https://doi.org/10.23919/acc.2017.7963833
  * [120] A. Liniger, “Path planning and control for autonomous racing,” Ph.D. dissertation, ETH Zurich, 2018. [Online]. Available: http://hdl.handle.net/20.500.11850/302942
  * [121] A. Liniger and J. Lygeros, “Real-time control for autonomous racing based on viability theory,” _IEEE Transactions on Control Systems Technology_ , vol. 27, no. 2, pp. 464–478, Mar. 2019. [Online]. Available: https://doi.org/10.1109/tcst.2017.2772903
  * [122] ——, “A noncooperative game approach to autonomous racing,” _IEEE Transactions on Control Systems Technology_ , vol. 28, no. 3, pp. 884–897, May 2020. [Online]. Available: https://doi.org/10.1109/tcst.2019.2895282
  * [123] Y. Liu, L. Shi, W. Xu, X. Xiong, W. Sun, and L. Qu, “Design of driverless racing chassis based on MPC,” in _2020 Chinese Automation Congress (CAC)_. IEEE, Nov. 2020. [Online]. Available: https://doi.org/10.1109/cac51589.2020.9326482
  * [124] D. Loiacono, A. Prete, P. L. Lanzi, and L. Cardamone, “Learning to overtake in TORCS using simple reinforcement learning,” in _IEEE Congress on Evolutionary Computation_. IEEE, Jul. 2010. [Online]. Available: https://doi.org/10.1109/cec.2010.5586191
  * [125] S. Lovato and M. Massaro, “Three-dimensional fixed-trajectory approaches to the minimum-lap time of road vehicles,” _Vehicle System Dynamics_ , pp. 1–18, Aug. 2021. [Online]. Available: https://doi.org/10.1080/00423114.2021.1969024
  * [126] Y. Mahmoud, Y. Okuyama, T. Fukuchi, T. Kosuke, and I. Ando, “Optimizing deep-neural-network-driven autonomous race car using image scaling,” _SHS Web of Conferences_ , vol. 77, p. 04002, 2020. [Online]. Available: https://doi.org/10.1051/shsconf/20207704002
  * [127] F. Massa, L. Bonamini, A. Settimi, L. Pallottino, and D. Caporale, “LiDAR-based GNSS denied localization for autonomous racing cars,” _Sensors_ , vol. 20, no. 14, p. 3992, Jul. 2020. [Online]. Available: https://doi.org/10.3390/s20143992
  * [128] D. Metz and D. Williams, “Near time-optimal control of racing vehicles,” _Automatica_ , vol. 25, no. 6, pp. 841–857, Nov. 1989. [Online]. Available: https://doi.org/10.1016/0005-1098(89)90052-6
  * [129] M. Montani, L. Ronchi, R. Capitani, and C. Annicchiarico, “A hierarchical autonomous driver for a racing car: Real-time planning and tracking of the trajectory,” _Energies_ , vol. 14, no. 19, p. 6008, Sep. 2021. [Online]. Available: https://doi.org/10.3390/en14196008
  * [130] S. Nekkah, J. Janus, M. Boxheimer, L. Ohnemus, S. Hirsch, B. Schmidt, Y. Liu, D. Borbély, F. Keck, K. Bachmann, and L. Bleszynski, “The autonomous racing software stack of the kit19d,” 2020.
  * [131] J. Ni and J. Hu, “Path following control for autonomous formula racecar: Autonomous formula student competition,” in _2017 IEEE Intelligent Vehicles Symposium (IV)_. IEEE, Jun. 2017. [Online]. Available: https://doi.org/10.1109/ivs.2017.7995972
  * [132] J. Ni, J. Hu, and C. Xiang, “Robust path following control at driving/handling limits of an autonomous electric racecar,” _IEEE Transactions on Vehicular Technology_ , vol. 68, no. 6, pp. 5518–5526, Jun. 2019. [Online]. Available: https://doi.org/10.1109/tvt.2019.2911862
  * [133] J. Niu, Y. Hu, B. Jin, Y. Han, and X. Li, “Two-stage safe reinforcement learning for high-speed autonomous racing,” in _2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)_. IEEE, Oct. 2020. [Online]. Available: https://doi.org/10.1109/smc42975.2020.9283053
  * [134] F. Nobis, J. Betz, L. Hermansdorfer, and M. Lienkamp, “Autonomous racing: A comparison of slam algorithms for large scale outdoor environment,” in _Proceedings of the 2019 3rd International Conference on Virtual and Augmented Reality Simulations_. ACM, Feb. 2019. [Online]. Available: https://doi.org/10.1145/3332305.3332319
  * [135] G. Notomista, M. Wang, M. Schwager, and M. Egerstedt, “Enhancing game-theoretic autonomous car racing using control barrier functions,” in _2020 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, May 2020. [Online]. Available: https://doi.org/10.1109/icra40945.2020.9196757
  * [136] T. Novi, A. Liniger, R. Capitani, and C. Annicchiarico, “Real-time control for at-limit handling driving on a predefined path,” _Vehicle System Dynamics_ , vol. 58, no. 7, pp. 1007–1036, Apr. 2019. [Online]. Available: https://doi.org/10.1080/00423114.2019.1605081
  * [137] M. O’Kelly, V. Sukhil, H. Abbas, J. Harkins, C. Kao, Y. V. Pant, R. Mangharam, D. Agarwal, M. Behl, P. Burgio, and M. Bertogna, “F1/10: An open-source autonomous cyber-physical platform,” 2019.
  * [138] M. O’Kelly, H. Zheng, D. Karthik, and R. Mangharam, “F1tenth: An open-source evaluation environment for continuous control and reinforcement learning,” in _Proceedings of the NeurIPS 2019 Competition and Demonstration Track_ , ser. Proceedings of Machine Learning Research, H. J. Escalante and R. Hadsell, Eds., vol. 123. PMLR, 08–14 Dec 2020, pp. 77–89. [Online]. Available: http://proceedings.mlr.press/v123/o-kelly20a.html
  * [139] M. O'Kelly, H. Zheng, A. Jain, J. Auckley, K. Luong, and R. Mangharam, “TUNERCAR: A superoptimization toolchain for autonomous racing,” in _2020 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, May 2020\. [Online]. Available: https://doi.org/10.1109/icra40945.2020.9197080
  * [140] R. Oliveira, F. H. M. Rocha, L. Ott, V. Guizilini, F. Ramos, and V. Grassi, “Learning to race through coordinate descent bayesian optimisation,” in _2018 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, May 2018. [Online]. Available: https://doi.org/10.1109/icra.2018.8460735
  * [141] E. Pagot, M. Piccinini, and F. Biral, “Real-time optimal control of an autonomous RC car with minimum-time maneuvers and a novel kineto-dynamical model,” in _2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)_. IEEE, Oct. 2020. [Online]. Available: https://doi.org/10.1109/iros45743.2020.9340640
  * [142] P. R. Palafox, J. Betz, F. Nobis, K. Riedl, and M. Lienkamp, “SemanticDepth: Fusing semantic segmentation and monocular depth estimation for enabling autonomous driving in roads without lane lines,” _Sensors_ , vol. 19, no. 14, p. 3224, Jul. 2019. [Online]. Available: https://doi.org/10.3390/s19143224
  * [143] Y. Pan, C.-A. Cheng, K. Saigol, K. Lee, X. Yan, E. Theodorou, and B. Boots, “Agile autonomous driving using end-to-end deep imitation learning,” in _Robotics: Science and Systems XIV_. Robotics: Science and Systems Foundation, Jun. 2018. [Online]. Available: https://doi.org/10.15607/rss.2018.xiv.056
  * [144] H. Park and J. C. Gerdes, “Optimal tire force allocation for trajectory tracking with an over-actuated vehicle,” in _2015 IEEE Intelligent Vehicles Symposium (IV)_. IEEE, Jun. 2015. [Online]. Available: https://doi.org/10.1109/ivs.2015.7225820
  * [145] ——, “Analysis of feasible tire force regions for optimal tire force allocation with limited actuation,” _IEEE Intelligent Transportation Systems Magazine_ , vol. 9, no. 3, pp. 75–87, 2017. [Online]. Available: https://doi.org/10.1109/mits.2017.2709879
  * [146] S. Pedone and A. Fagiolini, “Racecar longitudinal control in unknown and highly-varying driving conditions,” _IEEE Transactions on Vehicular Technology_ , vol. 69, no. 11, pp. 12 521–12 535, Nov. 2020. [Online]. Available: https://doi.org/10.1109/tvt.2020.3023059
  * [147] W. zheng Peng, Y. hui Ao, J. hui He, and P. fei Wang, “Vehicle odometry with camera-lidar-IMU information fusion and factor-graph optimization,” _Journal of Intelligent & Robotic Systems_, vol. 101, no. 4, Apr. 2021\. [Online]. Available: https://doi.org/10.1007/s10846-021-01329-x
  * [148] D. Perez, Y. Saez, G. Recio, and P. Isasi, “Evolving a rule system controller for automatic driving in a car racing competition,” in _2008 IEEE Symposium On Computational Intelligence and Games_. IEEE, Dec. 2008. [Online]. Available: https://doi.org/10.1109/cig.2008.5035659
  * [149] E. Perot, M. Jaritz, M. Toromanoff, and R. D. Charette, “End-to-end driving in a realistic racing game with deep reinforcement learning,” in _2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)_. IEEE, Jul. 2017. [Online]. Available: https://doi.org/10.1109/cvprw.2017.64
  * [150] F. K. Pour, D. Theilliol, V. Puig, and G. Cembrano, “Health-aware control design based on remaining useful life estimation for autonomous racing vehicle,” _ISA Transactions_ , vol. 113, pp. 196–209, Jul. 2021. [Online]. Available: https://doi.org/10.1016/j.isatra.2020.03.032
  * [151] P. Puchtler and R. Peinl, “Evaluation of deep learning accelerators for object detection at the edge,” in _Lecture Notes in Computer Science_. Springer International Publishing, 2020, pp. 320–326. [Online]. Available: https://doi.org/10.1007/978-3-030-58285-2_29
  * [152] J. Quadflieg, M. Preuss, and G. Rudolph, “Driving faster than a human player,” in _Applications of Evolutionary Computation_. Springer Berlin Heidelberg, 2011, pp. 143–152. [Online]. Available: https://doi.org/10.1007/978-3-642-20525-5_15
  * [153] A. Remonda, S. Krebs, E. Veas, G. Luzhnica, and R. Kern, “Formula rl: Deep reinforcement learning for autonomous racing using telemetry data,” _Unpublished_ , 2019. [Online]. Available: http://rgdoi.net/10.13140/RG.2.2.30678.09283
  * [154] A. Remonda, E. Veas, and G. Luzhnica, “Comparing driving behavior of humans and autonomous driving in a professional racing simulator,” _PLOS ONE_ , vol. 16, no. 2, p. e0245320, Feb. 2021. [Online]. Available: https://doi.org/10.1371/journal.pone.0245320
  * [155] T. Renzler, M. Stolz, M. Schratter, and D. Watzenig, “Increased accuracy for fast moving LiDARS: Correction of distorted point clouds,” in _2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)_. IEEE, May 2020. [Online]. Available: https://doi.org/10.1109/i2mtc43012.2020.9128372
  * [156] U. Rosolia, A. Carvalho, and F. Borrelli, “Autonomous racing using learning model predictive control,” in _2017 American Control Conference (ACC)_. IEEE, May 2017. [Online]. Available: https://doi.org/10.23919/acc.2017.7963748
  * [157] U. Rosolia and F. Borrelli, “Learning model predictive control for iterative tasks: A computationally efficient approach for linear system,” _IFAC-PapersOnLine_ , vol. 50, no. 1, pp. 3142–3147, Jul. 2017. [Online]. Available: https://doi.org/10.1016/j.ifacol.2017.08.324
  * [158] U. Rosolia, X. Zhang, and F. Borrelli, “Robust learning model predictive control for iterative tasks: Learning from experience,” in _2017 IEEE 56th Annual Conference on Decision and Control (CDC)_. IEEE, Dec. 2017. [Online]. Available: https://doi.org/10.1109/cdc.2017.8263812
  * [159] U. Rosolia and F. Borrelli, “Learning how to autonomously race a car: A predictive control approach,” _IEEE Transactions on Control Systems Technology_ , vol. 28, no. 6, pp. 2713–2719, Nov. 2020. [Online]. Available: https://doi.org/10.1109/tcst.2019.2948135
  * [160] A. Rucco, G. Notarstefano, and J. Hauser, “An efficient minimum-time trajectory generation strategy for two-track car vehicles,” _IEEE Transactions on Control Systems Technology_ , vol. 23, no. 4, pp. 1505–1519, Jul. 2015. [Online]. Available: https://doi.org/10.1109/tcst.2014.2377777
  * [161] M. Salem, A. M. Mora, J. J. Merelo, and P. García-Sánchez, “Driving in TORCS using modular fuzzy controllers,” in _Applications of Evolutionary Computation_. Springer International Publishing, 2017, pp. 361–376. [Online]. Available: https://doi.org/10.1007/978-3-319-55849-3_24
  * [162] M. Salem, A. M. Mora, and J. J. Merelo, “The evolutionary race: Improving the process of evaluating car controllers in racing simulators,” in _2018 IEEE Conference on Computational Intelligence and Games (CIG)_. IEEE, Aug. 2018. [Online]. Available: https://doi.org/10.1109/cig.2018.8490364
  * [163] ——, “Beating uncertainty in racing bot evolution through enhanced exploration and pole position selection,” in _2019 IEEE Conference on Games (CoG)_. IEEE, Aug. 2019. [Online]. Available: https://doi.org/10.1109/cig.2019.8847998
  * [164] J. P. S. Mejia, P. A. Theodosis, and J. C. Gerdes, “Using a path-fitting algorithm to analyze the racing techniques of a skilled driver,” in _Volume 3: Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing; System Identification (Estimation for Automotive Applications, Modeling, Therapeutic Control in Bio-Systems); Variable Structure/Sliding-Mode Control; Vehicles and Human Robotics; Vehicle Dynamics and Control; Vehicle Path Planning and Collision Avoidance; Vibrational and Mechanical Systems; Wind Energy Systems and Control_. American Society of Mechanical Engineers, Oct. 2013. [Online]. Available: https://doi.org/10.1115/dscc2013-4106
  * [165] F. Sauerbeck, L. Baierlein, J. Betz, and M. Lienkamp, “A combined lidar-camera localization for autonomous race cars [ saeindextestsamplev2 2022-01-18t10:06:44.605496z],” _SAE International Journal of Connected and Automated Vehicles_ , vol. 5, no. 1, jan 2022. [Online]. Available: https://doi.org/10.4271/12-05-01-0006
  * [166] W. Schwarting, T. Seyde, I. Gilitschenski, L. Liebenwein, R. Sander, S. Karaman, and D. Rus, “Deep latent competition: Learning to race using visual control policies in latent space,” 2021.
  * [167] W. Schwarting, A. Pierson, S. Karaman, and D. Rus, “Stochastic dynamic games in belief space,” _IEEE Transactions on Robotics_ , pp. 1–16, 2021. [Online]. Available: https://doi.org/10.1109/tro.2021.3075376
  * [168] A. Sinha, M. O’Kelly, H. Zheng, R. Mangharam, J. Duchi, and R. Tedrake, “FormulaZero: Distributionally robust online adaptation via offline population synthesis,” in _Proceedings of the 37th International Conference on Machine Learning_ , ser. Proceedings of Machine Learning Research, H. D. III and A. Singh, Eds., vol. 119. PMLR, 13–18 Jul 2020, pp. 8992–9004. [Online]. Available: http://proceedings.mlr.press/v119/sinha20a.html
  * [169] Y. Song, H. Lin, E. Kaufmann, P. Duerr, and D. Scaramuzza, “Autonomous overtaking in gran turismo sport using curriculum reinforcement learning,” 2021\.
  * [170] N. A. Spielberg, M. Brown, N. R. Kapania, J. C. Kegelman, and J. C. Gerdes, “Neural network vehicle models for high-performance automated driving,” _Science Robotics_ , vol. 4, no. 28, p. eaaw1975, Mar. 2019. [Online]. Available: https://doi.org/10.1126/scirobotics.aaw1975
  * [171] S. S. Srinivasa, P. Lancaster, J. Michalove, M. Schmittle, C. Summers, M. Rockett, J. R. Smith, S. Choudhury, C. Mavrogiannis, and F. Sadeghi, “Mushr: A low-cost, open-source robotic racecar for education and research,” 2019.
  * [172] S. Srinivasan, I. Sa, A. Zyner, V. Reijgwart, M. I. Valls, and R. Siegwart, “End-to-end velocity estimation for autonomous racing,” _IEEE Robotics and Automation Letters_ , vol. 5, no. 4, pp. 6869–6875, Oct. 2020. [Online]. Available: https://doi.org/10.1109/lra.2020.3016929
  * [173] S. Srinivasan, S. N. Giles, and A. Liniger, “A holistic motion planning and control solution to challenge a professional racecar driver,” 2021.
  * [174] T. Stahl, A. Wischnewski, J. Betz, and M. Lienkamp, “ROS-based localization of a race vehicle at high-speed using LIDAR,” _E3S Web of Conferences_ , vol. 95, p. 04002, 2019. [Online]. Available: https://doi.org/10.1051/e3sconf/20199504002
  * [175] ——, “Multilayer graph-based trajectory planning for race vehicles in dynamic scenarios,” in _2019 IEEE Intelligent Transportation Systems Conference (ITSC)_. IEEE, Oct. 2019\. [Online]. Available: https://doi.org/10.1109/itsc.2019.8917032
  * [176] T. Stahl, M. Eicher, J. Betz, and F. Diermeyer, “Online verification concept for autonomous vehicles – illustrative study for a trajectory planning module,” in _2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)_. IEEE, Sep. 2020. [Online]. Available: https://doi.org/10.1109/itsc45102.2020.9294703
  * [177] T. Stahl and J. Betz, “An open-source scenario architect for autonomous vehicles,” in _2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER)_. IEEE, Sep. 2020. [Online]. Available: https://doi.org/10.1109/ever48776.2020.9243029
  * [178] T. Stahl and F. Diermeyer, “Online verification enabling approval of driving functions—implementation for a planner of an autonomous race vehicle,” _IEEE Open Journal of Intelligent Transportation Systems_ , vol. 2, pp. 97–110, 2021. [Online]. Available: https://doi.org/10.1109/ojits.2021.3078121
  * [179] K. Strobel, S. Zhu, R. Chang, and S. Koppula, “Accurate, low-latency visual perception for autonomous racing: Challenges, mechanisms, and practical solutions,” in _2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)_. IEEE, Oct. 2020. [Online]. Available: https://doi.org/10.1109/iros45743.2020.9341683
  * [180] J. K. Subosits and J. C. Gerdes, “From the racetrack to the road: Real-time trajectory replanning for autonomous driving,” _IEEE Transactions on Intelligent Vehicles_ , vol. 4, no. 2, pp. 309–320, Jun. 2019. [Online]. Available: https://doi.org/10.1109/tiv.2019.2904390
  * [181] J. Subosits and J. C. Gerdes, “Impacts of model fidelity on trajectory optimization for autonomous vehicles in extreme maneuvers,” _IEEE Transactions on Intelligent Vehicles_ , pp. 1–1, 2021. [Online]. Available: https://doi.org/10.1109/tiv.2021.3051325
  * [182] V. Sukhil and M. Behl, “Adaptive lookahead pure-pursuit for autonomous racing,” 2021.
  * [183] K. L. Talvala, K. Kritayakirana, and J. C. Gerdes, “Pushing the limits: From lanekeeping to autonomous racing,” _Annual Reviews in Control_ , vol. 35, no. 1, pp. 137–148, Apr. 2011. [Online]. Available: https://doi.org/10.1016/j.arcontrol.2011.03.009
  * [184] A. Tătulea-Codrean, T. Mariani, and S. Engell, “Design and simulation of a machine-learning and model predictive control approach to autonomous race driving for the f1/10 platform,” _IFAC-PapersOnLine_ , vol. 53, no. 2, pp. 6031–6036, 2020. [Online]. Available: https://doi.org/10.1016/j.ifacol.2020.12.1669
  * [185] P. A. Theodosis and J. C. Gerdes, “Generating a racing line for an autonomous racecar using professional driving techniques,” in _ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Volume 2_. ASMEDC, Jan. 2011. [Online]. Available: https://doi.org/10.1115/dscc2011-6097
  * [186] ——, “Nonlinear optimization of a racing line for an autonomous racecar using professional driving techniques,” in _Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control_. ASME, 2012. [Online]. Available: https://doi.org/10.1115/dscc2012-movic2012-8620
  * [187] hanqing TIAN, J. NI, and J. HU, “Autonomous driving system design for formula student driverless racecar,” in _2018 IEEE Intelligent Vehicles Symposium (IV)_. IEEE, Jun. 2018. [Online]. Available: https://doi.org/10.1109/ivs.2018.8500471
  * [188] H. Tian, J. Ni, Z. Li, and J. Hu, “Autonomous formula racecar: Overall system design and experimental validation,” 2020.
  * [189] E. Tramacere, S. Luciani, S. Feraco, A. Bonfitto, and N. Amati, “Processor-in-the-loop architecture design and experimental validation for an autonomous racing vehicle,” _Applied Sciences_ , vol. 11, no. 16, p. 7225, Aug. 2021. [Online]. Available: https://doi.org/10.3390/app11167225
  * [190] C. Vallon and F. Borrelli, “Task decomposition for iterative learning model predictive control,” in _2020 American Control Conference (ACC)_. IEEE, Jul. 2020. [Online]. Available: https://doi.org/10.23919/acc45564.2020.9147625
  * [191] M. I. Valls, H. F. Hendrikx, V. J. Reijgwart, F. V. Meier, I. Sa, R. Dube, A. Gawel, M. Burki, and R. Siegwart, “Design of an autonomous racecar: Perception, state estimation and system integration,” in _2018 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, May 2018. [Online]. Available: https://doi.org/10.1109/icra.2018.8462829
  * [192] B. van Niekerk, A. Damianou, and B. Rosman, “Online constrained model-based reinforcement learning,” 2020.
  * [193] J. L. Vazquez, M. Bruhlmeier, A. Liniger, A. Rupenyan, and J. Lygeros, “Optimization-based hierarchical motion planning for autonomous racing,” in _2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)_. IEEE, Oct. 2020. [Online]. Available: https://doi.org/10.1109/iros45743.2020.9341731
  * [194] R. Verschueren, M. Zanon, R. Quirynen, and M. Diehl, “Time-optimal race car driving using an online exact hessian based nonlinear MPC algorithm,” in _2016 European Control Conference (ECC)_. IEEE, Jun. 2016. [Online]. Available: https://doi.org/10.1109/ecc.2016.7810277
  * [195] C. Voser, R. Y. Hindiyeh, and J. C. Gerdes, “Analysis and control of high sideslip manoeuvres,” _Vehicle System Dynamics_ , vol. 48, no. sup1, pp. 317–336, Dec. 2010. [Online]. Available: https://doi.org/10.1080/00423111003746140
  * [196] E. Wachter, A. Schmeitz, F. Bruzelius, and M. Alirezaei, “Path control in limits of vehicle handling: A sensitivity analysis,” in _Lecture Notes in Mechanical Engineering_. Springer International Publishing, 2020, pp. 1089–1095. [Online]. Available: https://doi.org/10.1007/978-3-030-38077-9_126
  * [197] N. Wagener, C.-A. Cheng, J. Sacks, and B. Boots, “An online learning approach to model predictive control,” 2019.
  * [198] M. Wang, Z. Wang, J. Talbot, J. C. Gerdes, and M. Schwager, “Game theoretic planning for self-driving cars in competitive scenarios,” in _Robotics: Science and Systems XV, University of Freiburg, Freiburg im Breisgau, Germany, June 22-26, 2019_ , A. Bicchi, H. Kress-Gazit, and S. Hutchinson, Eds., 2019. [Online]. Available: https://doi.org/10.15607/RSS.2019.XV.048
  * [199] Z. Wang, R. Spica, and M. Schwager, “Game Theoretic Motion Planning for Multi-robot Racing,” in _Distributed Autonomous Robotic Systems_ , vol. 9. Springer, 2020, pp. 225–238. [Online]. Available: http://link.springer.com/10.1007/978-3-030-05816-6_16
  * [200] M. Wang, Z. Wang, J. Talbot, J. C. Gerdes, and M. Schwager, “Game-theoretic planning for self-driving cars in multivehicle competitive scenarios,” _IEEE Transactions on Robotics_ , pp. 1–13, 2021. [Online]. Available: https://doi.org/10.1109/tro.2020.3047521
  * [201] T. Weiss and M. Behl, “Deepracing: Parameterized trajectories for autonomous racing,” 2020.
  * [202] ——, “DeepRacing: A framework for autonomous racing,” in _2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)_. IEEE, Mar. 2020. [Online]. Available: https://doi.org/10.23919/date48585.2020.9116486
  * [203] G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, “Aggressive driving with model predictive path integral control,” in _2016 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, May 2016. [Online]. Available: https://doi.org/10.1109/icra.2016.7487277
  * [204] G. Williams, B. Goldfain, P. Drews, J. M. Rehg, and E. A. Theodorou, “Autonomous racing with autorally vehicles and differential games,” 2017.
  * [205] G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou, “Information theoretic MPC for model-based reinforcement learning,” in _2017 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, May 2017\. [Online]. Available: https://doi.org/10.1109/icra.2017.7989202
  * [206] G. Williams, B. Goldfain, P. Drews, J. M. Rehg, and E. A. Theodorou, “Best response model predictive control for agile interactions between autonomous ground vehicles,” in _2018 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, May 2018\. [Online]. Available: https://doi.org/10.1109/icra.2018.8462831
  * [207] G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, “Information-theoretic model predictive control: Theory and applications to autonomous driving,” _IEEE Transactions on Robotics_ , vol. 34, no. 6, pp. 1603–1622, Dec. 2018. [Online]. Available: https://doi.org/10.1109/tro.2018.2865891
  * [208] G. Williams, B. Goldfain, P. Drews, K. Saigol, J. Rehg, and E. Theodorou, “Robust sampling based model predictive control with sparse objective information,” in _Robotics: Science and Systems XIV_. Robotics: Science and Systems Foundation, Jun. 2018. [Online]. Available: https://doi.org/10.15607/rss.2018.xiv.042
  * [209] G. Williams, B. Goldfain, J. M. Rehg, and E. A. Theodorou, “Locally weighted regression pseudo-rehearsal for online learning of vehicle dynamics,” 2019.
  * [210] A. Wischnewski, T. Stahl, J. Betz, and B. Lohmann, “Vehicle dynamics state estimation and localization for high performance race cars,” _IFAC-PapersOnLine_ , vol. 52, no. 8, pp. 154–161, 2019. [Online]. Available: https://doi.org/10.1016/j.ifacol.2019.08.064
  * [211] A. Wischnewski, J. Betz, and B. Lohmann, “A model-free algorithm to safely approach the handling limit of an autonomous racecar,” in _2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)_. IEEE, Nov. 2019. [Online]. Available: https://doi.org/10.1109/iccve45908.2019.8965218
  * [212] ——, “Real-time learning of non-gaussian uncertainty models for autonomous racing,” in _2020 59th IEEE Conference on Decision and Control (CDC)_. IEEE, Dec. 2020. [Online]. Available: https://doi.org/10.1109/cdc42340.2020.9304230
  * [213] A. Wischnewski, M. Euler, S. Gümüs, and B. Lohmann, “Tube model predictive control for an autonomous race car,” _Vehicle System Dynamics_ , pp. 1–23, Jun. 2021. [Online]. Available: https://doi.org/10.1080/00423114.2021.1943461
  * [214] C. You and P. Tsiotras, “Vehicle modeling and parameter estimation using adaptive limited memory joint-state UKF,” in _2017 American Control Conference (ACC)_. IEEE, May 2017. [Online]. Available: https://doi.org/10.23919/acc.2017.7962973
  * [215] ——, “Real-time trail-braking maneuver generation for off-road vehicle racing,” in _2018 Annual American Control Conference (ACC)_. IEEE, Jun. 2018. [Online]. Available: https://doi.org/10.23919/acc.2018.8431620
  * [216] ——, “High-speed cornering for autonomous off-road rally racing,” _IEEE Transactions on Control Systems Technology_ , vol. 29, no. 2, pp. 485–501, Mar. 2021. [Online]. Available: https://doi.org/10.1109/tcst.2019.2950354
  * [217] D. Zadok, T. Hirshberg, A. Biran, K. Radinsky, and A. Kapoor, “Explorations and lessons learned in building an autonomous formula SAE car from simulations,” in _Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications_. SCITEPRESS - Science and Technology Publications, 2019. [Online]. Available: https://doi.org/10.5220/0008120604140421
  * [218] M. Zeillinger, R. Hauk, M. Bader, and A. Hofmann, “Design of an autonomous race car for the formula student driverless (fsd),” 2017. [Online]. Available: https://openlib.tugraz.at/download.php?id=5aaa45931188a&location=medra
  * [219] V. Zhang, S. Thornton, and C. Gerdes, “Tire modeling to enable model predictive control of automated vehicles from standstill to the limits of handling,” in _AVEC 18 -14th International Symposium on Advanced Vehicle Control_ , 2018. [Online]. Available: https://ddl.stanford.edu/sites/g/files/sbiybj9456/f/Zhang_2018_avec.pdf
  * [220] J. Zubaca, M. Stolz, and D. Watzenig, “Extended h$\infty$ filter adaptation based on innovation sequence for advanced ego-vehicle motion estimation,” in _2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)_. IEEE, Nov. 2020. [Online]. Available: https://doi.org/10.1109/cavs51000.2020.9334568
  * [221] ——, “Smooth reference line generation for a race track with gates based on defined borders,” in _2020 IEEE Intelligent Vehicles Symposium (IV)_. IEEE, Oct. 2020. [Online]. Available: https://doi.org/10.1109/iv47402.2020.9304722
  * [222] I. Zubov, I. Afanasyev, A. Gabdullin, R. Mustafin, and I. Shimchik, “Autonomous drifting control in 3d car racing simulator,” in _2018 International Conference on Intelligent Systems (IS)_. IEEE, Sep. 2018. [Online]. Available: https://doi.org/10.1109/is.2018.8710588
  * [223] C. Jung, S. Lee, H. Seong, A. Finazzi, and D. H. Shim, “Game-theoretic model predictive control with data-driven identification of vehicle model for head-to-head autonomous racing,” in _2021 International Conference on Robotics and Automation (ICRA 2021) - Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper1.pdf
  * [224] N. Li, E. Goubault, L. Pautet, and S. Putot, “Autonomous racecar control in head-to-head competition using mixed-integer quadratic programming,” in _2021 International Conference on Robotics and Automation (ICRA 2021) - Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper2.pdf
  * [225] M. Schratter, J. Zubaca, K. Mautner-Lassnig, T. Renzler, M. Kirchengast, S. Loigge, M. Stolz, and D. Watzenig, “Lidar-based mapping and localization for autonomous racing,” in _2021 International Conference on Robotics and Automation (ICRA 2021) - Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper4.pdf
  * [226] T. Weiss, J. Chrosniak, and M. Behl, “Towards multi-agent autonomous racing with the deepracing framework,” in _2021 International Conference on Robotics and Automation (ICRA 2021) - Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper5.pdf
  * [227] M. Schmid, Q. Zhu, A. Boncimino, R. Prucka, and C. Paredis, “Control informed design of the iac autonomous racecar for operation at the dynamic envelope,” in _2021 International Conference on Robotics and Automation (ICRA 2021) \- Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper6.pdf
  * [228] X. Xiao, J. Biswas, and P. Stone, “Learning inverse kinodynamics for accurate high-speed off-road navigation on unstructured terrain,” in _2021 International Conference on Robotics and Automation (ICRA 2021) - Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper7.pdf
  * [229] D. Kalaria, P. Maheshwari, A. Jha, A. Issar, D. Chakravarty, S. Anwar, and A. Tovar, “Local nmpc on global optimised path for autonomous racing,” in _2021 International Conference on Robotics and Automation (ICRA 2021) - Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper8.pdf
  * [230] J. Bhargav, J. Betz, H. Zheng, and R. Mangharam, “Track based offline policy learning for overtaking maneuvers with autonomous racecars,” in _2021 International Conference on Robotics and Automation (ICRA 2021) - Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper9.pdf
  * [231] S. Wadeka, B. Schwartz, A. Gamal, S. Kannan, M. Mar, R. Manna, V. Chellapandi, and D. Gonzalez, “Towards end-to-end deep learning for autonomous racing: On data collection and a unified architecture for steering and throttle prediction,” in _2021 International Conference on Robotics and Automation (ICRA 2021) - Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper10.pdf
  * [232] T. Brüdigam, A. Capone, S. Hirche, D. Wollherr, and M. Leibold, “Gaussian process-based stochastic model predictive control for overtaking in autonomous racing,” in _2021 International Conference on Robotics and Automation (ICRA 2021) - Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper11.pdf
  * [233] R. Wang, Y. Han, and U. Vaidya, “Deep koopman data-driven control framework forautonomous racing,” in _2021 International Conference on Robotics and Automation (ICRA 2021) - Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper12.pdf
  * [234] M. Jiang, K. Miller, D. Sun, Z. Liu, Y. Jia, A. Datta, N. Ozay, and S. Mitra, “Continuous integration and testing for autonomous racing software: An experience report from graic,” in _2021 International Conference on Robotics and Automation (ICRA 2021) - Workshop Opportunities and Challenges With Autonomous Racing_. IEEE, 2021. [Online]. Available: https://linklab-uva.github.io/icra-autonomous-racing/contributed_papers/paper13.pdf
  * [235] S. D. Pendleton, H. Andersen, X. Du, X. Shen, M. Meghjani, Y. H. Eng, D. Rus, and M. H. Ang, “Perception, planning, control, and coordination for autonomous vehicles,” _Machines_ , vol. 5, no. 1, 2017. [Online]. Available: https://www.mdpi.com/2075-1702/5/1/6
  * [236] W. Hess, D. Kohler, H. Rapp, and D. Andor, “Real-time loop closure in 2d lidar slam,” in _2016 IEEE International Conference on Robotics and Automation (ICRA)_ , 2016, pp. 1271–1278.
  * [237] E. Espié, C. Guionneau, B. Wymann, C. Dimitrakakis, R. Coulom, and A. Sumner, “Torcs, the open racing car simulator,” 2005.
  * [238] R. Pütz and T. Serné, _Rennwagentechnik - Praxislehrgang Fahrdynamik_. Springer Fachmedien Wiesbaden, 2017. [Online]. Available: https://doi.org/10.1007/978-3-658-16102-6
  * [239] A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in _Proceedings of the 1st Annual Conference on Robot Learning_ , 2017, pp. 1–16.
  * [240] G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” _arXiv preprint arXiv:1606.01540_ , 2016.
  * [241] A. Domahidi and J. Jerez, “Forces professional,” Embotech AG, url=https://embotech.com/FORCES-Pro, 2014–2019.
  * [242] D. Ha and J. Schmidhuber, “World models,” 2018.

|  Johannes Betz earned both a B. Eng. (2011) and a M. Sc. (2012) in the field
of Automotive Engineering. After he did is PhD at the Technical University of
Munich (TUM) he was Postdoctoral Researcher at the Institute of Automotive
Technology at TUM where he founded the TUM Autonomous Motorsport Team. He is
now a postdoctoral researcher at the University of Pennsylvania where he is
working at the xLab for Safe Autonomous Systems. His research is focusing on a
holistic software development for autonomous systems with extreme motions at
the dynamic limits in extreme and unknown environments. By using modern
algorithms from the field of artificial intelligence he is trying to develop
new and advanced methods and intelligent algorithms. Based on his additional
studies in philosophy he extends current path and behavior planners for
autonomous systems with ethical theories
---|---
|  Hongrui Zheng received his B.S. degrees in Mechanical Engineering and
Computer Science from Georgia Institute of Technology, and his M.S. degree in
Robotics from University of Pennsylvania. He is currently a Ph.D. candidate at
the University of Pennsylvania where he is working at the xLab for Safe
Autonomous Systems. His research focuses on building the tools and theoretical
foundations necessary to scale design, testing, and optimization of safe-
autonomous systems.
---|---
|  Alexander Liniger received the B.Sc. and M.Sc. degrees in mechanical
engineering from the Department of Mechanical and Process Engineering, ETH
Zurich, Switzerland, in 2010 and 2013, respectively, and in May 2018
successfully defended his PhD degree at the Automatic Control Laboratory, ETH
Zurich, Switzerland. Currently he is a Postdoctoral Researcher in the Computer
Vision Lab, ETH Zurich, Switzerland, where he is part of Luc van Gool’s group
working in the Toyta TRACE project. During his PhD his main research interests
include model predictive control, viability theory as well as game theory and
their application to autonomous driving and racing. Currently, he is
investigating how control theory and computer vision can be combined to
achieve end-to-end learning approaches with formal guarantees.
---|---
|  Ugo Rosolia is a postdoctoral scholar at Caltech, working with Prof. Aaron
Ames and Prof. Yisong Yue. His current research focuses on high-level planning
in partially observable environments and on designing control algorithms that
allow autonomous systems to perform highly dynamical maneuvers while
guaranteeing safety. During his PhD at the University of California Berkeley,
he worked with Prof. Francesco Borrelli in the MPC lab. He developed the
Learning Model Predictive Control (LMPC) strategy, which is a model-based
policy iteration strategy. This strategy was used to teach an autonomous
vehicle how to race.
---|---
|  Phillip Karle is currently a PhD student at the Technical University of
Munich. He received both his B.Sc. degree in 2017 and his M.Sc. degree in 2019
from the Technical University of Munich (TUM), Munich, Germany, where he is
currently working towards his Ph.D. in mechanical engineering at the Institute
of Automotive Technology. His main research interests include data-mining,
scenario understanding, motion prediction and related applications for
autonomous driving with the focus on unstructured environments.
---|---
|  Madhur Behl is an assistant professor in the departments of Computer
Science, and Systems and Information Engineering, and a member of the Cyber-
Physical Systems Link Lab at the University of Virginia. He conducts research
at the confluence of Machine Learning, Predictive Control, and Artificial
Intelligence with applications in Cyber-Physical Systems, Autonomous Systems,
Robotics, and Smart Cities. He received his Ph.D. (2015) and M.S. (2012), in
Electrical and Systems Engineering, both from the University of Pennsylvania.
---|---
|  Venkat Krovi is the Michelin Endowed Chair of Vehicle Automation in the
Departments of Automotive Engineering and Mechanical Engineering at Clemson
University. He received his Ph.D. degree in Mechanical Engineering and Applied
Mechanics from the University of Pennsylvania in 1998. The underlying theme of
his research activities has been to take advantage of the “power of the many”
(both autonomous-agents and humans) to extend the reach of human users in the
dull, dirty, and dangerous environments. These efforts at analyzing and
realizing the potential of distributed-autonomy and human-robot synergy has
unlocked new opportunities in wide ranging applications in plant-automation,
consumer electronics, automobile, defense and healthcare/surgical simulation
arenas.
---|---
|  Rahul Mangharam received his Ph.D. in Electrical and Computer Engineering
from Carnegie Mellon University where he also received his MS and BS. He is an
Associate Professor in the Department of Electrical and Systems Engineering at
the University of Pennsylvania. He is a founding member of the PRECISE Center
and directs the xLab for Safe Autonomous Systems Lab at Penn. His research is
at the intersection of formal methods, machine learning and control for
medical devices, energy efficient buildings, and autonomous systems.
---|---