# Monoscopic vs. Stereoscopic Views and Display Types in the Teleoperation of
Unmanned Ground Vehicles for Object Avoidance

Yiming Luo1, Jialin Wang1, Hai-Ning Liang1,∗, Shan Luo2, and Eng Gee Lim3 The
work has been supported in part by Xi’an Jiaotong-Liverpool University (XJTLU)
Key Special Fund (KSF-A-03; KSF-P-02) and XJTLU Research Development
Fund.1Yiming Luo, Jialin Wang, Hai-Ning Liang are with the Department of
Computing, Xi’an Jiaotong-Liverpool University, Suzhou, China.2Shan Luo is
with the Department of Computer Science, The University of Liverpool, UK.3Eng
Geen Lim is with The Department of Communications and Networking, Xi’an
Jiaotong-Liverpool University, Suzhou, China.∗Corresponding author
(haining.liang@xjtlu.edu.cn).

###### Abstract

Virtual reality (VR) head-mounted displays (HMD) have recently been used to
provide an immersive, first-person vision/view in real-time for manipulating
remotely-controlled unmanned ground vehicles (UGV). The teleoperation of UGV
can be challenging for operators when it is done in real time. One big
challenge is for operators to perceive quickly and rapidly the distance of
objects that are around the UGV while it is moving. In this research, we
explore the use of monoscopic and stereoscopic views and display types
(immersive and non-immersive VR) for operating vehicles remotely. We conducted
two user studies to explore their feasibility and advantages. Results show a
significantly better performance when using an immersive display with
stereoscopic view for dynamic, real-time navigation tasks that require
avoiding both moving and static obstacles. The use of stereoscopic view in an
immersive display in particular improved user performance and led to better
usability.

Figure 1: The four display modes tested in the first experiment. (A, a) D1: a
regular monitor; using one of the binocular cameras’ images. (B, b) D2: a
fixed virtual screen in VR; using one of the binocular cameras’ images. (C, c)
D3: a virtual screen in VR moving along with head motions; using one of the
binocular cameras’ images (monoscopic). (D, d) D4: a virtual screen in VR
moving along with head motions; using both of the binocular cameras’ images
(stereoscopic).(e) The picture of UGV with the camera facing a board on the
monitor.

##  I Introduction

The teleoperation of unmanned ground vehicles (UGV) or unmanned aerial
vehicles (UAV) is challenging for operators because it is not easy to have a
good understanding of the terrain and surrounding objects given that they are
away from the actual environment [1]. With advances in real-time image
transmission, issues such as low image quality, long delays in image
transmission, and unstable signal transmission have been improved
significantly, making their teleoperation stable and practical in a wide range
of situations [2]. These improvements have allowed for the exploration of
different image views that can be shown on different display types to improve
the ability of operators to control UGV in a precise and efficient manner.

Current displays used in the teleoperation of UGV include primarily
traditional 2D screens (such as those on mobile phones/tablets and typical
desktop computers). More recently immersive displays, such as virtual reality
head-mounted displays (VR HMD), have started to make their way for UGV
teleoperations. VR HMD allow a higher level of immersion and presence (that
is, the feeling of being “there” in the environment) [3], which can enhance
performance [4, 5]. The disadvantage of VR HMD is that they have small
displays and their use may increase the chance of operators having motion
sickness, a common issue with HMD that is not present in normal displays [6,
7].

Whether it is a traditional screen or a VR HMD, it is not easy for operators
to gauge the distance and depth perception of objects from the video images
shown in the displays in real time because, unlike being in the real
environment, they often do not provide enough visual stereoscopic information
for the operators to see.

Depending on the placement of the camera in the drone, the operator can see
images based on the first-person view (FPV) or the third-person view (TPV) [1,
8, 9]. Mainstream drones in the market can supply a FPV perspective primarily.
Based on cost factors and the stability of image transmission, most drones use
a single camera, typically of high-quality, to transmit monoscopic images to a
2D non-immersive screen display. In the absence of stereoscopic cues, these 2D
images do not often provide enough visual information for operators to
perceive clearly and accurately distance and depth of obstacles surrounding
the drone[1, 10]. This situation could be worse when there are moving objects
while the drone is moving towards them.

Using binocular cameras could provide additional information to assist
operators gauge distance information of objects. The use of binocular cameras
has been studied on UAV mainly but not so much on UGV [1, 10, 11, 12]. At low
altitudes but high speed, a UAV with an on-board computer that can provide
stereoscopic FPV rendered in a VR HMD could enhance the control of the flying
drone [1]. In our research, we explore the use of binocular cameras in UGV,
which tend to go slower than UAV and could have more objects on its path, and
whether they can enhance teleporters’ stereoscopic perception and their
performance in obstacle avoidance tasks in real-time. In addition, we want to
compare the usability and performance in both normal displays and immersive VR
HMD.

In this paper, we first review related work about viewing modes and display
types as well as obstacle avoidance tasks and their performance metrics. We
then present two user studies. The first study explores four viewing modes in
a normal 2D display and VR HMD (see Fig. 1). We compared these modes based on
participants’ subjective feedback and objective results in a pre-designed
obstacle avoidance experiment. The version with stereoscopic view in VR had
best performance but participants still faced challenges with moving objects.
These results led us modify this viewing mode and run a second study comparing
the new mode against it. The results show that the new fifth mode could lead
to an improved overall performance. The results of these two studies can
inform the design of future teleoperator-UGV interaction that offers better
performance and user experience.

##  II Related Work

In this section, we first introduce previous work on viewing modes and display
types for UGV/UAV. Then we present the tasks for testing robot performance and
the metrics for their evaluation.

###  II-A Viewing Modes and Display Types

Viewing modes (e.g., 2D vs. 3D) and display types (VR vs. 2D displays) can
affect users’ levels of immersion, flow, and performance [4, 13, 14]. Most
teleoperated unmanned systems use a monocular camera that displays video
streams captured from the camera attached to a robot. The camera supplies
images for operators to see and make decisions for where to go. Because the
camera tends to have a small field-of-view (FOV), the video images could only
provide limited visual details which in turn can lead to lower performance on
tasks such as target detection and identification [15]. It requires operators
to put extra effort to survey the environment (e.g., by manipulating and/or
rotating the robot to get different views) [16], an inefficient process that
can increase mental and visual load and the feeling of motion sickness. Also,
important distance cues may not be provided while depth perception may be
degraded when the FOV is restricted [17].

When driving a UGV, operators have more difficulties in judging the speed of
the vehicle, time-to-collision, perception of objects, locations of obstacles,
and the start of a sharp curve [18]. The level of difficulty can increase if
the objects around the drone are moving and not static. However, simply
increasing the FOV may lead to other issues. For example, in a typical 2D
display, this could mean that the users may need to scan a wide view of the
environment, requiring moving their head left and right frequently. In
addition, a larger FOV may cause additional usability issues with VR HMD as
rotation head movements might induce greater motion sickness [6, 7]. The
challenge is to provide additional visual cues without significantly changing
the FOV and causing large head movements, especially rotational ones.

3D Stereoscopic (3DS) views have been found to be better than traditional non-
stereo (2D) views on manipulation tasks with either virtual or real objects
[5, 19]. In [1] the authors explored immersive 3DS views for flying drones
which enable higher accurate depth perception and led to better teleoperation
and navigation performance. Stereoscopic FPV presents significant advantages
over monocular FPV[20, 21]. The distance between the binocular cameras used to
achieve 3DS also has an important effect. The best performance was achieved
when the inter-camera distance was less than the inter-ocular distance, which
is 2–3 cm and 6 cm, respectively [22]. However, artificially induced binocular
stereo-vision may increase motion sickness and perceived stress [23]. Latency
in image transmission is also associated with motion sickness [24, 25]. Low
image quality caused by reduced frames per second (fps), reduced resolution of
the display (pixels per frame), while a lower gray-scale (number of levels of
brightness or bits per frame) [26] can also increase the motion sickness.
While motion sickness can be an issue and when it is possible to have high-
resolution images without transmission delays [16], the depth information and
stereoscopic perception provided by the binocular cameras can significantly
improve the operator’s teleoperation performance for obstacle avoidance and
precise maneuverability.

Distance underestimation and overestimation can occur when objects are viewed
in normal displays and especially in immersive VR environments (VE) [22, 27].
In experiments with teleoperated UGV, it has been found that operators
underestimated the distances from obstacles and landmarks [28]. As such, given
the benefits of 3DS views in VR, this research aims to explore whether the
combination of 3DS and VR could support operators to gauge distance
information during teleoperation manipulation and improve their performance in
obstacle avoidance tasks.

###  II-B Tasks and Metrics

Teleoperation tasks that require real-time obstacle avoidance have been
researched in the context of human-drone interaction. For example, in [29,
30], researchers explored a remotely controlled robot that has been integrated
with a laser sensor and a monocular camera to capture distance information.
This combination provided operators with images displayed in a virtual UI. The
operator was asked to drive this robot through a series of mazes and avoid
obstacles. Its performance was measured using a set of metrics [31]: (a)
Obstacle encounter, the number of collisions of the robot against obstacles;
(b) Efficiency, the time to complete tasks; and (c) Subjective ratings,
usability issues with controls and the interface. We followed a similar
approach and used these metrics in this research.

In addition, it has been shown that moving objects require a greater depth
information of the objects to avoid colliding with them [32]. As such, the
maze used in our research (see Fig. 3 on the next page) incorporated both
static and dynamic objects of different types. Their combination allows us to
explore in detail the comparative performance of teleoperating a UGV based on
different viewing modes and display types.

##  III User Study 1

To explore how monoscopic and stereoscopic views and display types affect the
distance perception of users, we conducted a 4-condition, 8-person within-
subjects study. The main task consisted of participants driving a UGV in real
time using a game controller through a maze that had both static and moving
obstacles.

Figure 2: (a) The prototype used in this research. It contains a UGV (DJI
RoboMaster S1) and a FPV system which was built with a binocular camera, an
on-board mini desktop, and a power bank. (b) Implementation of 3DS view.

Figure 3: Overview of the maze. (a) The distribution of the different tasks in
the maze (T1, T2, T3, T4, and T5); (b) A picture of the actual maze used in
the study.

Figure 4: Overview of the components of the control and image transmission
system.

###  III-A Prototype

Fig. 2a shows the prototype we developed to conduct this research. A DJI
RoboMaster s1111DJI RoboMaster S1: https://www.dji.com/robomaster-s1 was used
as the mobile UGV. The transmission system consisted of a binocular camera, a
mini desktop, and a power bank. The first-person view (FPV) images from the
camera were transmitted to a PC and rendered in a traditional 27-inch 4K
monitor or VR HMD, which in our case was a HTC Vive Pro Eye222HTC Vive
Pro:https://www.vive.com/uk/product/vive-pro-eye/specs/.

The binocular camera consists of 2 monocular cameras with a resolution of 1280
× 720, a 71° FOV, and distortion less than ± 0.3 % [33]. The binocular camera
has a stereoscopic view with a 2560 × 720 resolution. It was installed on a
mounting platform with Inter Pupillary Distance (IPD) adjustment with a range
of about 26mm to 84mm. The average human IPD is 65mm, with a range of 54 to 75
millimeter for young adults, between 16 to 24 years [34]. Therefore, the IPD
was set to 65mm in the stereoscopic FPV condition. The right camera was moved
to a central position as the input source of the other monoscopic conditions.
The FPV system used WebRTC as the video streaming protocol which has a 214 ± 7
(ms) latency at 2560 x 720 resolution (1280 x 720 per eye) and 30 FPS in a Wi-
Fi6 connection [35]. A mini desktop with 16GB RAM, an i7-10710U CPU, and an
Intel UHD Graphics was installed on the robot as the WebRTC server which was
powered by a 100w power bank. The HMD and monitor were connected to a desktop
with 16GB RAM, an i7-9700k CPU, a GeForce GTX 2080Ti dedicated GPU.

Participants used an Xbox wireless controller333Xbox wireless controller:
https://www.microsoft.com/en-us/p/xbox-wireless-
controller/8xn59crbsqgz?cid=msft_web_collection. as the input device to
control the UGV. Fig. 4 (next page) shows the elements of the control and
image transmission system consisting of a custom control protocol, robot
controller software for Windows, Inter-Process-Communication (IPC), VR and PC
interface built in Unity3D, and Xbox controller for controlling the robot
remotely.

###  III-B Conditions

In this study, the following four conditions were explored (see Fig. 1). Fig.
1e shows the UGV’s camera pointing at a board with black dots and words to
show the difference between monoscopic and stereoscopic images.

  * •

D1: Non-immersive display with fixed screen using monoscopic images (see Fig.
1A, 1a). We used a traditional 27-inch monitor as the display screen. The
content of the display was from monoscopic images (fed from one of the
binocular cameras placed on the robot). This is the non-immersive display and
the baseline condition as this is what is provided typically in current
UGV/UAV via a mobile phone, tablet or regular desktop.

  * •

D2: Immersive display with fixed screen using monoscopic images (see Fig. 1B,
1b). We constructed a fixed virtual screen in VR. All irrelevant information
is blacked out, except for what is displayed. This condition is the monoscopic
view in VR.

  * •

D3: Immersive display with head tracking screen using monoscopic images (see
Fig. 1C, 1c). We had this condition because this is the same as the ones that
are used for current UGV or UGV that provide VR capabilities. We turned the
fixed screen in into a moving screen that follows users’ head motion. It used
monoscopic images captured from one of the binocular cameras.

  * •

D4: Immersive display with head tracking screen using stereoscopic images (see
Fig. 1d). We used the same moving screen as D3, but the content of display was
stereoscopic images captured using both binocular cameras. Each camera would
provide images to the corresponding eye (see Fig. 2b).

###  III-C Tasks and Procedures

To investigate the performance of the four different types of views and
display methods, participants had to drive the robot through a customized maze
(Fig. 3b). We installed a high-definition video camera and 8 motion tracking
VICON444Vicon system: https://www.vicon.com/ cameras to capture the movement
of the robot along the maze and to detect any collisions with the objects in
the maze.

For each condition, participants had to drive the robotic car remotely and
maneuver it through the maze as fast as possible but without hitting or
colliding with the obstacles. The driving maze was designed to have five
different tasks (see Fig. 3a):

  * •

T1: This task consisted of 36 small and static wooden cubes. As the UGV moves
deeper into the maze, the distance between the two cubes would become smaller
(from 32cm to 30cm to 28cm).

  * •

T2: This task had two big cubes placed horizontally. The user needs to drive
UGV in a side-way manner to prevent collisions.

  * •

T3: This task had a cardboard box that rotated at a constant speed.
Participants would need to judge distance and time their move accordingly to
avoid colliding with this spinning obstacle.

  * •

T4: This task had a circular moving obstacle and a cross shaped, static
obstacle. Participants had to judge the distance between the robot and the
obstacles and time its move accordingly to avoid hitting both the moving
obstacle and horizontal cross at the same time.

  * •

T5: This task consisted of an obstacle that would move in a straight line in a
backward and forward manner and a cube placed vertically. Participants needed
to judge the robot’s distance and time its move to avoid colliding with either
object.

A simple driving training outside the maze was given to the participants
before they started the formal trials. The purpose of this training was to
give participants the chance to become familiar with the controls, the HMD,
and controlling the robot. After this training, the participants were asked to
run the formal trials. Each participant had three trials in each display
methods and the order of display methods was pre-determined by a 4 × 4 Latin
square to reduce any learning effect.

###  III-D Participants

Eight participants (5 males and 3 females, aged between 20-29, mean = 24.5)
were recruited for this experiment. Data collected from the pre-experiment
demographics questionnaire show that that they all declared to be healthy and
did not have any health issues, physical and otherwise. They all had normal or
corrected-to-normal vision and did not suffer from any known motion sickness
issues in their normal daily activities. None of them had any experience
driving a UGV using HMD in FPV. As such, it was the first time for all 8
participants to drive a remote a UGV using an HMD in FPV.

This experiment has been approved by the University Ethics Committee at Xi’an
Jiaotong-Liverpool University.

###  III-E Hypotheses

Based on our review of the literature and experiment design, we formulated the
following four hypotheses:

  * •

H1: D4 would lead to the best overall performance in distance perception; D1
would lead to the worse overall performance than D2 and D3;

  * •

H2: D4 would lead to the best local performance in the complex tasks (T3, T4,
and T5); D1 would lead to the worse local performance than D2 and D3 in these
three tasks (T3, T4, and T5);

  * •

H3: D4 would significantly reduce user demands and would be the most popular
display method; D1 would have more user demands and less user preferences than
D2 and D3.

###  III-F Results

####  III-F1 Overall Performance

All participants understood the nature of the tasks and all recorded data were
valid. If there was a collision and it lasted less than 1 second, then it was
considered as one collision only. If it lasted longer than 1 second, for each
1 second of collision time, we counted it as one collision. We recorded the
number of collisions for each trial by checking frame by frame the high-
definition videos from the camera and VICON tracking system. A Shapiro-Wilk
test for normality was performed on each of measures separately for each
condition and show that they followed a normal distribution.

Fig. 5a shows the average number of collisions per condition. A repeated
measures ANOVA with Greenhouse-Geisser correction showed that the mean of the
number of collisions differed significantly between display types (F(1.722,
12.054) = 10.691, p <.05). A Bonferroni post-hoc test revealed that the number
of collisions was significantly lower for D2 and D4 (p <.05) compared to D1.
There was no significant difference between D2, D3 and D4 (p >.05).

A repeated measures ANOVA with Greenhouse-Geisser correction found that there
was no significant difference between conditons (F(1.481, 10.365) = 1.327, p
>.05).

####  III-F2 Local Performance

Fig. 5b shows the average number of collisions for each condition in each
task. A repeated measures ANOVA with Greenhouse-Geisser correction found that
there was a significant difference in T1 (F(1.464, 10.248) = 14.477, p <.05).
A Bonferroni post-hoc test showed that the number of collisions was
significantly lower in T3 for D1 (p <.05) and D3 (p <.001) when compared to
C4.

There was no significant difference among D1, D2, D3 and D4 in completion
time.

###  III-G Subjective Results

Figure 5: (a) Overall mean and std. deviation of number of collisions for the
four conditions (D1, D2, D3, and D4); (b) Local mean and std. deviation of
number of collisions for the four conditions (D1, D2, D3, and D4) in each task
(T1, T2, T3, T4, T5). All Error bars show +1 SD; (c) Average of response
scores for each element of UEQ. Error bars show +1 SD. Higher scores represent
more preferences in all cases. The ’$\times$’ symbol represents the mean
value. Error bars show $\pm$1 SD.

####  III-G1 NASA-TLX Workload

Kruskal-Wallis H Test was conducted and found no significant difference in any
sub-scales of the NASA-TLX workload (Mental, Physical, Temporal, Effort,
Performance, and Frustration).

####  III-G2 User Experience Questionnaire (UEQ)

Fig. 5c shows the results of the UEQ. They were evaluated with the Kruskal-
Wallis H Test comparing the four conditions for the six UEQ elements
(Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and
Novelty). There was a significant difference in Novelty ($\chi^{2}$(3) =
9.077, p <.05). Dunn’s post-hoc analysis showed a significant difference (p
<.05) for D1 vs D4 in Novelty.

####  III-G3 Interviews Results

Overall, all participants had a positive experience in the experiment. None of
the participants commented that they had any serious discomfort or simulation
sickness in the four conditions. Most said that the most difficult task was
T3, the section with a cardboard box that rotated at a constant speed.

###  III-H Discussion

The results confirmed some of our initial hypotheses but also revealed some
different effects. We discuss these next.

####  III-H1 Overall Performance

From the evaluation of the overall number of collisions, we have following
effects:

  * •

D2 significantly reduced the number of collisions compared to D1. This
supports in part H1.

  * •

D3 did not significantly reduce the number of collisions compared to D1. This
seems to contradict H1.

  * •

D4 significantly reduced the number of collisions compared to D1. This also
supports in part H1.

D2, D3, and D4 all used VR. Their advantage over a normal monitor was that
they could increase immersion and eliminate the reflection of light on the 2D
display screen. Participants seem to have had better concentration and
immersion when the off-screen information was all blackout. According to H1,
all conditions with VR (D2, D3, and D4) should have improved the performance
compared to D1. However, we only found significant improvements in D2 vs D1
and D4 vs D1. This seems to indicate that the existing display modes from
products in the market (D3 vs D1), as mentioned in Section II.A (Condition),
did not show significant difference in performance for real-time obstacle
avoidance tasks. That is, they did not lead to a good performance overall.

For overall completion time, our analysis indicated that the different
conditions did not significantly improve efficiency in the tasks, which
contradicted H1.

In interviews after the experiment, participants commented that they needed
more effort in T3 because estimating the distance of the UGV to a moving
obstacle with frequent changes in depth perception was very challenging.

####  III-H2 Local Performance

Users behaved differently in different tasks in our experiment. Based on the
analysis of collisions, we found that only one task (T3) presented significant
differences on performance, while the other tasks did not, which supported
part of H2 (only T3 showed differences). In particular, we found the
following:

  * •

Using the virtual screen in VR (D2) and monoscopic view in VR (D3) did not
significantly reduce the number of collisions compared to the 2D screen (D1)
in T3.

  * •

Having stereoscopic images in VR (D4) significantly reduced the number of
collisions compared to the 2D screen (D1) and the monoscopic view in VE (D3),
respectively.

T3 required participants to judge the distance between the UGV and a spinning
box. The difference between this task (T3) and other tasks with dynamic
obstacles (T4 and T5) was that it not only changed the motion states of the
obstacle but also significantly changed the depth of the obstacle that user
needed to perceive from the views. This was where the advantages of 3DS view
was observed the most because it allowed participants to obtain a better depth
perception and 3D effect. Therefore, the participants could have a better
sense of the distance between the UGV and the obstacles, which helped improve
their performance. This was supported by the interview data. Participants
commented that they needed more effort on T3 because estimating the distance
to a moving obstacle with frequent changes in perceptual depth was quite
challenging.

####  III-H3 User Demands and User Preferences

The positive results from NASA-TLX data do not fully show equal preference of
users for the four conditions, which contradicted the first part of H3. The
results of UEQ gave us further insights into participants’ preferences. From
the results of UEQ, D4 was considered more creative and innovative when
compared to D1, which supported the second part of H3.

##  IV User Study 2

Based on the above results of Study 1, we combined D2 and D4 to create a new
display mode - D5. This version would show participants a fixed big screen in
VR and allow them to have 3DS vision by giving each eye different images from
the corresponding binocular camera. The only difference between D4 and D5 was
whether the screen was movable or fixed (see Fig. 6).

Figure 6: (a) A picture of the UGV’s camera(s) watching a board. (b) View of
the left camera of the binocular cameras. (c) View of the right camera of the
binocular cameras. (d) Images for the two eyes in D4 and (e) in D5.

Figure 7: (a) Overall mean and std. deviation of number of collisions for the
two conditions (D4 and D5); (b) Local mean and std. deviation of number of
collisions for the two conditions (D4 and D5). All Error bars show +1 SD; (c)
Average of response scores for each element of NASA-TLX workload. Higher
scores represent more demands in all cases; (d) Average of response scores for
each element of UEQ. Higher scores represent more preferences in all cases.
The ’$\times$’ symbol represents the mean value. Error bars show $\pm$1 SD.

###  IV-A Conditions

We decided to compare the best-performing version (D4) in Study 1 with the new
mode (D5):

  * •

D4: Immersive display with head tracking screen using stereoscopic images. The
same as in Study 1.

  * •

D5: Immersive display with fixed screen using stereoscopic images. We used a
fixed virtual screen in VR. The display content was stereoscopic (using both
binocular cameras; each camera provides different images to their
corresponding eye).

###  IV-B Participants

Another 8 participants (4 males and 4 females, aged between 21-27, mean =
22.5) were recruited for this experiment. Data from the pre-experiment
questionnaire show that none of the participants had major physical
discomfort, health problems, simulator sickness, or vision issues. All of them
were able to complete the pre-training successfully. Similar to participants
in Study 1, none of these participants had any experience with driving a UGV
using HMD in FPV. As such, it was also the first time for all 8 participants
to drive a UGV using an HMD in FPV.

###  IV-C Experiment setup

The rest of experiment setup was the same as in Study 1.

###  IV-D Hypotheses

Based on Study 1, we formulated the following two hypotheses:

  * •

H4: According to the objective results of Study 1, D5 would perform better
than D4 overall and especially in T3.

  * •

H5: According to the subjective results of Study 1, D5 would be more preferred
by participants than D4.

###  IV-E Results

A Shapiro-Wilk test for normality was performed on each of measures separately
for each condition and showed that they all followed a normal distribution.
Fig. 7a and 7b show the performance of the two modes. From these figures, we
can see that:

  * •

For overall performance, the independent-samples t-test showed that D5
significantly reduced the number of collisions (t(78) = 3.473, p <.001)
compared to D4.

  * •

In local performance, the t-test showed that D5 significantly reduced the
number of collisions compared to D4 in T1 (t(14)=3.473 = 3.631, p <.05), T3
(t(14) = 2.395, p <.05), and T5 (t(14) = 3.147, p <.05).

Fig. 7c and 7d show the summary of the NASA-TLX and UEQ questionnaire data. An
analysis shows that:

  * •

There was a significant difference in the NASA-TLX workload in Effort
($\chi^{2}$(1) = 7.345, p <.05). Participants needed more effort using D4.

  * •

There was also a significant difference in the UEQ data in Attractiveness
($\chi^{2}$(1) = 10.678, p <.05). Participants preferred D5 significantly
more.

###  IV-F Discussion

From the results, we can observe that participants performed better in D5, as
indicated by an overall lower number of collisions (especially in T3). This
result confirms H4. However, we also found that D5 had better performance in
T1 and T5, which we had not thought it would be the case. The NASA-TLX results
also show a similar trend. Participants did not need much effort to finish the
tasks using D5. In addition, the UEQ data showed that participants
significantly liked D5 more, which supported H5. All the above results show
that D5 led to better performance than D4 in overall (local) performance,
required lower demands from participants, and led to an enhanced user
experience. These results suggest that an immersive display with a fixed
screen using stereoscopic images (like D5) is a viable approach to allow
operators remotely control a UGV when obstacle avoidance is essential.

##  V Limitations and Future Work

From the results of the two studies, we can observe that an immersive display
with stereoscopic images can significantly increase user performance in
obstacle avoidance tasks. They also indicate that a fixed screen in VR led to
a better performance than a screen that moves together with head motions.

This research has the following two limitations, which can serve as directions
for future work. Even with binocular cameras, it is still not possible to
significantly increase the environmental information because of the
limitations of the camera’s FOV. If users do not manipulate the camera, they
are not able to see a wide view of the environment on a single screen. Cameras
that can capture wide views, including 360° panoramic ones, around the robot
could be one possible solution but further research is needed to assess their
suitability because such views will require users to move their heads
frequently, which could lead to higher levels of motion sickness.

In this research, we explored views on two types of displays and did not
investigate image distortion or enhancement approaches. Given that the
immersive VR display with stereoscopic view led to better performance and
higher usability, it will be interesting to see if distorting the images (to
enhance certain elements for example) could allow even better performance.
Applying edge enhancement could potentially improve the perception of the
contours of obstacles and may let operators gauge distance information in a
more precise way. This line of research could produce useful and interesting
results and applications.

In addition, another way to help improve manoeuvrability to avoid obstacles is
to use operators’ physiological information that can be captured during
teleoperation. For example, eye gaze is readily available and implicitly
provided by operators. Recent research shows that gaze data can be used to
improve object manipulation in VR environments [36]. Typically, when
approaching an obstacle to be avoided, operators’ gaze would likely be fixated
on that object and, when a fixation is longer than a threshold, the system can
provide additional information to help steer the UGV towards the most optimal
path. As eye trackers are becoming a common feature of HMD, using gaze data
could represent a low-cost and efficient approach for improving operator-UGV
interaction. Further research is needed to explore how this can be achieved.

##  VI Conclusion

In this paper, we have explored four viewing/display modes for real-time
unmanned ground vehicle (UGV) control in obstacle avoidance tasks. The aim is
to investigate whether these modes allow users to determine UGV-to-obstacle
distances. Study 1 evaluated the performance and user preference of the four
modes in a maze with moving and static objects that the UGV had to avoid.
Results from this study show that the version with stereoscopic images
displayed in a virtual reality head-mounted display (VR HMD) led to better
performance and usability. In Study 2, a new display mode that combines two
modes from Study 1 was compared with the best-performing mode (VR with
stereoscopic view). Overall, our results indicate that an immersive VR display
with a fixed screen using stereoscopic images is an applicable and suitable
approach for improving depth perception when controlling a UGV in real-time in
obstacle avoidance tasks, whether static or moving. It also helped
participants lower their workload levels and led to an enhanced user
experience.

## Acknowledgment

The authors would like to thank the participants for their time and the
reviewers for their reviews and useful comments.

## References

  * [1] N. Smolyanskiy and M. Gonzalez-Franco, “Stereoscopic first person view system for drone navigation,” _Frontiers in Robotics and AI_ , vol. 4, p. 11, 2017\.
  * [2] J. S. McCarley and C. D. Wickens, “Human factors concerns in uav flight,” _University of Illinois at Urbana-Champaign Institute of Aviation, Aviation Human Factors Division_ , 2004.
  * [3] R. Pausch, D. Proffitt, and G. Williams, “Quantifying immersion in virtual reality,” in _Proceedings of the 24th annual conference on Computer graphics and interactive techniques_ , 1997, pp. 13–18.
  * [4] W. Xu, H.-N. Liang, K. Yu, and N. Baghaei, “Effect of gameplay uncertainty, display type, and age on virtual reality exergames,” in _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ , 2021, pp. 1–14.
  * [5] J. P. McIntire, P. R. Havig, and E. E. Geiselman, “Stereoscopic 3d displays and human performance: A comprehensive review,” _Displays_ , vol. 35, no. 1, pp. 18–26, 2014.
  * [6] A. B. Oving and J. B. van Erp, “Driving with a head-slaved camera system,” in _Proceedings of the Human Factors and Ergonomics Society Annual Meeting_ , vol. 45, no. 18. SAGE Publications Sage CA: Los Angeles, CA, 2001, pp. 1372–1376.
  * [7] C. Smyth, V. Paul, A. Meldrum, and K. McDowell, “Examining alternative display configurations for an indirect vision driving interface,” _US Army Research Laboratory: Aberdeen Proving Ground, MD, to be published_.
  * [8] D.-H. Kim, Y.-G. Go, and S.-M. Choi, “First-person-view drone flying in mixed reality,” in _SIGGRAPH Asia 2018 Posters_ , 2018, pp. 1–2.
  * [9] A. C. Plascencia, V. Beran, and K. Sedlmajer, “Drone sensory data processing for advanced drone control for augmented reality.” 2019.
  * [10] Z. Chen, X. Luo, and B. Dai, “Design of obstacle avoidance system for micro-uav based on binocular vision,” in _2017 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII)_. IEEE, 2017, pp. 67–70.
  * [11] H. Duan, H. Li, Q. Luo, C. Zhang, C. Li, P. Li, and Y. Deng, “A binocular vision-based uavs autonomous aerial refueling platform,” _Science China Information Sciences_ , vol. 59, no. 5, pp. 1–7, 2016.
  * [12] Y. Ma, Q. Li, L. Chu, Y. Zhou, and C. Xu, “Real-time detection and spatial localization of insulators for uav inspection based on binocular stereo vision,” _Remote Sensing_ , vol. 13, no. 2, p. 230, 2021.
  * [13] W. Xu, H.-N. Liang, Z. Zhang, and N. Baghaei, “Studying the effect of display type and viewing perspective on user experience in virtual reality exergames,” _Games for health journal_ , vol. 9, no. 6, pp. 405–414, 2020\.
  * [14] D. Monteiro, H.-N. Liang, J. Wang, H. Chen, and N. Baghaei, “An in-depth exploration of the effect of 2d/3d views and controller types on first person shooter games in virtual reality,” in _2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)_. IEEE, 2020, pp. 713–724.
  * [15] R. P. Darken, K. Kempster, and B. Peterson, “Effects of streaming video quality of service on spatial comprehension in a reconnaissance task,” 2001.
  * [16] J. Y. Chen, E. C. Haas, and M. J. Barnes, “Human performance issues and user interface design for teleoperated robots,” _IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)_ , vol. 37, no. 6, pp. 1231–1245, 2007.
  * [17] B. G. Witmer and W. J. Sadowski Jr, “Nonvisually guided locomotion to a previously viewed target in real and virtual environments,” _Human factors_ , vol. 40, no. 3, pp. 478–488, 1998.
  * [18] J. B. Van Erp and P. Padmos, “Image parameters for driving with indirect viewing systems,” _Ergonomics_ , vol. 46, no. 15, pp. 1471–1499, 2003.
  * [19] D. Drascic, “An investigation of monoscopic and stereoscopic video for teleoperation.” 1993.
  * [20] ——, “Skill acquisition and task performance in teleoperation using monoscopic and stereoscopic video remote viewing,” in _Proceedings of the Human Factors Society Annual Meeting_ , vol. 35, no. 19. SAGE Publications Sage CA: Los Angeles, CA, 1991, pp. 1367–1371.
  * [21] J. Y. Chen, E. C. Haas, and M. J. Barnes, “Human performance issues and user interface design for teleoperated robots,” _IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)_ , vol. 37, no. 6, pp. 1231–1245, 2007.
  * [22] D. R. Lampton, D. P. McDonald, M. Singer, and J. P. Bliss, “Distance estimation in virtual environments,” in _Proceedings of the human factors and ergonomics society annual meeting_ , vol. 39, no. 20. SAGE Publications Sage CA: Los Angeles, CA, 1995, pp. 1268–1272.
  * [23] D. R. Scribner and J. W. Gombash, “The effect of stereoscopic and wide field of view conditions on teleoperator performance,” ARMY RESEARCH LAB ABERDEEN PROVING GROUND MD HUMAN RESEARCH AND ENGINEERING …, Tech. Rep., 1998.
  * [24] K. M. Stanney, R. R. Mourant, and R. S. Kennedy, “Human factors issues in virtual environments: A review of the literature,” _Presence_ , vol. 7, no. 4, pp. 327–351, 1998.
  * [25] E. M. Kolasinski, _Simulator sickness in virtual environments_. US Army Research Institute for the Behavioral and Social Sciences, 1995, vol. 1027.
  * [26] A. Rastogi, “Design of an interface for teleoperation in unstructured environments using augmented reality displays.” 1997.
  * [27] B. G. Witmer and P. B. Kline, “Judging perceived and traversed distance in virtual environments,” _Presence_ , vol. 7, no. 2, pp. 144–167, 1998.
  * [28] D. E. McGovern, “Experience and results in teleoperation of land vehicles,” in _Pictorial communication in virtual and real environments (2nd ed.)_ , 1993, pp. 182–195.
  * [29] C. W. Nielsen, B. Ricks, M. A. Goodrich, D. Bruemmer, D. Few, and M. Few, “Snapshots for semantic maps,” in _2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583)_ , vol. 3. IEEE, 2004, pp. 2853–2858.
  * [30] B. Ricks, C. W. Nielsen, and M. A. Goodrich, “Ecological displays for robot interaction: A new perspective,” in _2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566)_ , vol. 3. IEEE, 2004, pp. 2855–2860.
  * [31] A. Steinfeld, T. Fong, D. Kaber, M. Lewis, J. Scholtz, A. Schultz, and M. Goodrich, “Common metrics for human-robot interaction,” in _Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction_ , 2006, pp. 33–40.
  * [32] J. O. Merritt, V. G. CuQlock-Knopp, M. Kregel, J. Smoot, and W. Monaco, “Perception of terrain drop-offs as a function of lr viewpoint separation in stereoscopic video,” in _Helmet-and Head-Mounted Displays X: Technologies and Applications_ , vol. 5800. International Society for Optics and Photonics, 2005, pp. 169–176.
  * [33] J. Weng, P. Cohen, M. Herniou _et al._ , “Camera calibration with distortion models and accuracy evaluation,” _IEEE Transactions on pattern analysis and machine intelligence_ , vol. 14, no. 10, pp. 965–980, 1992\.
  * [34] H. B. Pryor, “Objective measurement of interpupillary distance,” _Pediatrics_ , vol. 44, no. 6, pp. 973–977, 1969.
  * [35] B. Sredojev, D. Samardzija, and D. Posarac, “Webrtc technology overview and signaling solution design and implementation,” in _2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO)_. IEEE, 2015, pp. 1006–1009.
  * [36] D. Yu, X. Lu, R. Shi, H.-N. Liang, T. Dingler, E. Velloso, and J. Goncalves, “Gaze-supported 3d object manipulation in virtual reality,” in _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ , 2021, pp. 1–13.


SNSN-323-63

$B\rightarrow K^{(*)}\ell^{+}\ell^{-}$ from B-factories and Tevatron

Gerald Eigen
representing the BABAR collaboration 111Work supported by the Norwegian
Research Council.

Department of Physics and Technology
University of Bergen, 5007 Bergen, NORWAY

> BABAR and Belle measurements of branching fractions, rate asymmetries and
> angular observables in the decay modes $B\rightarrow
> K^{(*)}\ell^{+}\ell^{-}$ are reviewed and new results from CDF on
> $B\rightarrow K^{(*)}\mu^{+}\mu^{-}$ branching fractions and angular
> observables are discussed. A first search for $B^{+}\rightarrow
> K^{+}\tau^{+}\tau^{-}$ is presented.

> PRESENTED AT
>
>
>
>
> CKM workshop 2010
> Warwick, UK, September 06–10, 2010

##  1 Introduction

The decays $b\rightarrow s\ell^{+}\ell^{-}$, where $\ell^{+}\ell^{-}$ is an
$e^{+}e^{-},\mu^{+}\mu^{-}$ or $\tau^{+}\tau^{-}$ pair, are flavor-changing
neutral current (FCNC) processes, which are forbidden in the Standard Model
(SM) at tree level but are allowed to proceed via electroweak loops and weak
box diagrams. An effective Hamiltonian is used to calculate decay amplitudes
[1], which depend on three effective Wilson coefficients, $C_{7}^{eff}$,
$C_{9}^{eff}$, and $C_{10}^{eff}$. The first is extracted from the
$B\rightarrow X_{s}\gamma$ branching fraction, the latter two respectively
represent the vector and axial vector part of the weak penguin and box
diagrams. New Physics effects involve new loops that interfere with the SM
processes modifying the measured values of $C_{7}^{eff}$, $C_{9}^{eff}$, and
$C_{10}^{eff}$ with respect to the SM predictions [2]. In addition, scalar and
pseudoscalar processes may contribute that introduce new Wilson coefficients
$C_{s}$ and $C_{p}$ that are forbidden in the SM. Thus, it is important to
measure many observables in order to overconstrain the complex Wilson
coefficients [3]. These electroweak penguin modes contribute in probing New
Physics at a scale of a few TeV [4]. In this review, we focus on exclusive
decays presenting results from BABAR , Belle and CDF. The data samples are
based on luminosities of $349~{}\rm fb^{-1}$, $605~{}\rm fb^{-1}$ and
$4.4~{}\rm fb^{-1}$ corresponding to 384 million $B\overline{B}$ events, 656
Million $B\overline{B}$ events and $2\times 10^{10}~{}b\overline{b}$ events,
respectively.

##  2 Selection of $B\rightarrow K^{(*)}e^{+}e^{-}$ and $B\rightarrow
K^{(*)}\mu^{+}\mu^{-}$ Events

BABAR and Belle fully reconstruct ten $B\rightarrow K^{(*)}e^{+}e^{-}$ and
$B\rightarrow K^{(*)}\mu^{+}\mu^{-}$ final states, in which a
$K^{+},K^{0}_{S},K^{+}\pi^{-},K^{+}\pi^{0}$ or $K^{0}_{S}\pi^{+}$ recoils
against the lepton pair***Charge conjugation is implied unless otherwise
stated., while CDF reconstructs $K^{+}\mu^{+}\mu^{-}$ and
$K^{+}\pi^{-}\mu^{+}\mu^{-}$ final states. BABAR (Belle) selects lepton
candidates with momenta $p_{e}>0.3(0.4)~{}\rm GeV/c$ and
$p_{\mu}>0.7(0.7)~{}\rm GeV/c$. BABAR and Belle require good particle
identification (PID) for $e,\mu,K$, and $\pi$, and select $K^{0}_{S}$ in the
$\pi^{+}\pi^{-}$ channel. CDF requires muons with $p_{T}(\mu)>0.4~{}\rm
GeV/c$, kaons and pions with $p_{T}(K,\pi)>\rm 1~{}GeV/c$ and $B$-mesons with
$p_{T}(B)>6~{}\rm GeV/c$. Both, muons and hadrons must have good PID and the
muon pair must originate from a secondary vertex. All three experiments
suppress combinatorial $B\overline{B}$ and $q\overline{q}$ continuum
backgrounds ($q=u,d,s,c)$. Here, the leptons dominantly originate from
semileptonic $b$ and $c$ decays. BABAR trains neural networks (NN) using event
shape variables, vertex information, missing energy, and lepton separation
near the interaction region (IR) optimized in each mode and each $q^{2}$
bin†††This is the squared momentum transfer into the dilepton system.. Belle
trains a Fisher discriminant using event shape variables, missing mass, B
flavor tagging, and lepton separation in z near the IR. CDF trains NNs using
vertex information, the angle between the signed vertex displacement with
respect to the B momentum, and the $\mu$ separation. BABAR and Belle select
signal candidates using the beam-energy substituted mass
$m_{ES}=\sqrt{E^{*2}_{beam}-p^{*2}_{B}}$ and the energy difference $\Delta
E=E^{*}_{B}-E^{*}_{beam}$, where $E^{*}_{beam},E^{*}_{B}$ and $p^{*}_{B}$ are
the beam energy, B-meson energy and B-meson momentum in the $\mathchar
28935\relax(4S)$ center-of-mass frame, respectively. BABAR extracts the signal
yield from a one-dimensional unbinned extended maximum log-likelihood fit in
$m_{ES}$, while Belle performs a one (two) dimensional unbinned extended
maximum log-likelihood fit in $m_{ES}$ (and $m_{K\pi}$) for
$K^{(*)}\ell^{+}\ell^{-}$ modes. CDF selects signal candidates from an
unbinned maximum log-likelihood fit in the B invariant-mass distribution. All
experiments reject events in the $J/\psi$ and $\psi(2S)$ mass regions and
require that $K\mu$ and $K\pi\mu$ masses are not consistent with a $D$ mass to
reject background from $B\rightarrow DX$ decays. The rejected charmonium
events are used as control samples for various cross checks.

##  3 Results for $B\rightarrow K^{(*)}e^{+}e^{-}$ and $B\rightarrow
K^{(*)}\mu^{+}\mu^{-}$ Modes

Figure 1 (left) shows total branching fractions for $B\rightarrow
K^{(*)}\ell^{+}\ell^{-}$ ($e^{+}e^{-}$ and $\mu^{+}\mu^{-}$ modes combined)
[6, 7, 8] and $B\rightarrow X_{s}\ell^{+}\ell^{-}$[9, 7] in comparison to the
SM predictions [5]. The individual exclusive measurements are summarized in
Table 1. The Belle inclusive measurement is a recent update based on a
luminosity of $\rm 605~{}fb^{-1}$, yielding ${\cal B}(B\rightarrow
X_{s}\ell^{+}\ell^{-})=3.33\pm 0.8^{+0.19}_{-0.24})\times 10^{-6}$ [10]. The
partial branching fractions measured in the three experiments are also
consistent with the SM predictions.

Experiment | Mode | ${\cal B}~{}[10^{-6)}]$ | ${\cal A}_{CP}$ | ${\cal R}_{K^{(*)}}$
---|---|---|---|---
BABAR [6] | $K\ell^{+}\ell^{-}$ | $0.394^{+0.073}_{-0.069}\pm 0.02$ | $-0.18^{+0.18}_{-0.18}\pm 0.01$ | $0.96^{+0.44}_{-0.34}\pm 0.05$
BABAR [6] | $K^{*}\ell^{+}\ell^{-}$ | $1.11^{+0.19}_{-0.18}\pm 0.07$ | $-0.01^{+0.16}_{-0.15}\pm 0.01$ | $1.10^{+0.42}_{-0.32}\pm 0.07$
Belle [7] | $K\ell^{+}\ell^{-}$ | $0.48^{+0.05}_{-0.04}\pm 0.03$ | $0.04\pm 0.1\pm 0.02$ | $1.03\pm 0.19\pm 0.06$
Belle [7] | $K^{*}\ell^{+}\ell^{-}$ | $1.07^{+0.11}_{-0.10}\pm 0.09$ | $-0.10\pm 0.1\pm 0.01$ | $0.83\pm 0.17\pm 0.8$
CDF [8] | $K\mu^{+}\mu^{-}$ | $0.38^{+0.05}_{-0.05}\pm 0.03$ |  |
CDF [8] | $K^{*}\mu^{+}\mu^{-}$ | $1.06^{+0.14}_{-0.14}\pm 0.09$ |  |
Table 1: Branching fractions, $C\\!P$ asymmetries and lepton flavor ratios for
$B\rightarrow K^{(*)}\ell^{+}\ell^{-}$ modes in the entire $q^{2}$ region from
BABAR, Belle, and CDF. Uncertainties are statistical and systematic,
respectively.

Figure 1: (Left) Total branching fractions measurements of $B\rightarrow
K^{(*)}\ell^{+}\ell^{-}$ and $B\rightarrow X_{s}\ell^{+}\ell^{-}$ modes from
BABAR (red dots), Belle (blue triangles) and CDF (magenta squares) in
comparison to the SM prediction (grey-shaded region). For BABAR and Belle,
$\ell^{+}\ell^{-}$ is a combination of $e^{+}e^{-}$ and $\mu^{+}\mu^{-}$
modes, for CDF it is $\mu^{+}\mu^{-}$. (Right) Isospin asymmetry measurements
for $B\rightarrow K^{(*)}\ell^{+}\ell^{-}$ versus $q^{2}$ from BABAR (black
squares, blue dots) and Belle (red triangles, green triangles).

Rate asymmetries are more precisely measured than branching fractions, since
many uncertainties cancel [11]. The isospin asymmetry [12]

${\cal A}_{I}(q^{2})=\frac{d{\cal B}(B^{0}\rightarrow
K^{(*)0}\ell^{+}\ell^{-})/dq^{2}-(\tau_{B^{0}}/\tau_{B^{+}})d{\cal
B}(B^{+}\rightarrow K^{(*)+}\ell^{+}\ell^{-})/dq^{2}}{d{\cal
B}(B^{0}\rightarrow
K^{(*)0}\ell^{+}\ell^{-})/dq^{2}+(\tau_{B^{0}}/\tau_{B^{+}})d{\cal
B}(B^{+}\rightarrow K^{(*)+}\ell^{+}\ell^{-})/dq^{2}},$ (1)

corrected for the different $B^{0}$ and $B^{+}$ lifetimes
($\tau_{B^{0}}/\tau_{B^{+}}$), is expected to be small in the SM ($d{\cal
A}_{I}(q^{2})/dq^{2}$ is $-0.01$ for $q^{2}=2.7-6~{}\rm GeV^{2}/c^{4}$ after
dropping from $\simeq 0.075$ at $q^{2}=0.1~{}\rm GeV^{2}/c^{4}$ and crossing
zero near $q^{2}=1.7~{}\rm GeV^{2}/c^{4}$) [12]. Figure 1 (right) shows the
BABAR and Belle ${\cal A}_{I}$ measurements for different $q^{2}$ regions. The
$q^{2}$ integrated isospin asymmetry and ${\cal A}_{I}$ for $q^{2}$ values
above the $J/\psi$ are consistent with the SM prediction. Below the $J/\psi$,
however, BABAR observes a negative ${\cal A}_{I}$ that deviates significantly
from the SM prediction ($3.9\sigma$ from ${\cal A}_{I}=0$) . For models in
which the sign in $C^{eff}_{7}$ is flipped with respect to the value in the
SM, a small negative ${\cal A}_{I}$ is expected [12, 13], but it is too small
to explain the BABAR measurement. For low $q^{2}$, the Belle results are
consistent with both BABAR and the SM.

In the SM, the direct $C\\!P$ asymmetry

${\cal A}_{CP}=\frac{{\cal B}(\overline{B}\rightarrow
K^{(*)}\ell^{+}\ell^{-})-{\cal B}(B\rightarrow K^{(*)}\ell^{+}\ell^{-})}{{\cal
B}(\overline{B}\rightarrow K^{(*)}\ell^{+}\ell^{-})+{\cal B}(B\rightarrow
K^{(*)+}\ell^{+}\ell^{-})}.$ (2)

is expected to be ${\cal O}(10^{-3})$, and new physics at the electroweak
scale may provide significant enhancements [14]. BABAR performs a simultaneous
fit to $B^{+}\rightarrow K^{+}\ell^{+}\ell^{-}$ and $B\rightarrow
K^{*}\ell^{+}\ell^{-}$ modes. The results summarized in Table 1 together with
Belle’s measurements are consistent with the SM expectations.

In the SM, the lepton flavor ratios ${\cal R}_{K}={\cal B}(B\rightarrow
K\mu^{+}\mu^{-})/{\cal B}(B\rightarrow Ke^{+}e^{-})$ and ${\cal
R}_{K^{*}}={\cal B}(B\rightarrow K^{*}\mu^{+}\mu^{-})/{\cal B}(B\rightarrow
K^{*}e^{+}e^{-})$ integrated over all $q^{2}$ are predicted to be one and
0.75, respectively. The theoretical uncertainties are just a few percent. For
example, in two-Higgs-doublet models the presence of a SUSY Higgs might give
$\sim 10\%$ corrections to ${\cal R}_{K^{(*)}}$ for large $\tan\beta$ [13].The
BABAR and Belle measurements summarized in Table 1 are consistent with the SM
expectations.

The $B\rightarrow K^{*}\ell^{+}\ell^{-}$ angular distribution depends on three
angles: $\theta_{K}$, the angle between the K momentum and the B momentum in
the $K^{*}$ rest frame, $\theta_{\ell}$, the angle between the
$\ell^{+}(\ell^{-})$ momentum and the $B(\overline{B})$ momentum in the
$\ell^{+}\ell^{-}$ rest frame, and $\phi$, the angle between the two decay
planes. The angular distribution involves 12 $q^{2}$-dependent coefficients
$J_{i}$ [15, 16] that can be extracted from a full angular fit in individual
bins of $q^{2}$. Since large data samples are necessary for this study, BABAR
, Belle and CDF have analyzed only the one-dimensional angular distributions

$\displaystyle W(\cos\theta_{K})=\frac{3}{2}{\cal
F}_{L}\cos^{2}\theta_{K}+\frac{3}{4}(1-{\cal F}_{L})\sin^{2}\theta_{K},\ \ \ \
\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $ (3) $\displaystyle
W(\cos\theta_{\ell})=\frac{3}{4}{\cal
F}_{L}\sin^{2}\theta_{\ell}+\frac{3}{8}(1-{\cal
F}_{L})(1+\cos^{2}\theta_{\ell})+{\cal A}_{FB}\cos\theta_{\ell},$ (4)

where ${\cal F}_{L}$ is the $K^{*}$ longitudinal polarization and ${\cal
A}_{FB}$ is the lepton forward-backward asymmetry. While Belle and CDF measure
${\cal F}_{L}$ and ${\cal A}_{FB}$ in six $q^{2}$ bins, BABAR measured ${\cal
F}_{L}$ and ${\cal A}_{FB}$ in two $q^{2}$ bins due to the limited data
sample. An update with the full BABAR data set in six $q^{2}$ bins is in
progress. The measured $m_{ES}$ and angular distributions are fitted with
signal, combinatorial background and peaking background components. After
determining the signal yield from the $m_{ES}$ spectrum, ${\cal F}_{L}$ is
extracted from a fit to the $\cos\theta_{K}$ distribution for fixed signal
yield. Finally, ${\cal A}_{FB}$ is extracted from the $\cos\theta_{\ell}$
distribution for fixed signal yield and fixed ${\cal F}_{L}$.

Figure 2 shows the BABAR, Belle, and CDF results for ${\cal F}_{L}$ (left) and
${\cal A}_{FB}$ (right) in comparison to the SM prediction (lower red curve)
[18] and for flipped-sign $C^{eff}_{7}$ models (upper blue curve) [20, 23]. In
the SM, ${\cal A}_{FB}$ is negative for small $q^{2}$, crosses zero at
$q^{2}_{0}=(4.2\pm 0.6)~{}\rm GeV^{2}/c^{4}$ and is positive for large
$q^{2}$, while for flipped-sign $C^{eff}_{7}$ models ${\cal A}_{FB}$ is
positive for all $q^{2}$. Table 2 summarized the ${\cal F}_{L}$ and ${\cal
A}_{FB}$ measurements from $B\rightarrow K^{*}\ell^{+}\ell^{-}$ in the low
$q^{2}$ region in comparison to the SM prediction. For ${\cal F}_{L}$, the
three measurements are consistent with each other and the SM prediction. For
${\cal A}_{FB}$, the three measurements are in good agreement. Though they are
in better agreement with the flipped-sign $C^{eff}_{7}$ model, they are
consistent with the SM prediction. For $B\rightarrow K\ell^{+}\ell^{-}$,
${\cal A}_{FB}$ is consistent with zero as expected in the SM.

Experiment |  $q^{2}$ bin $\rm[GeV^{2}/c^{4}]$ | ${\cal F}_{L}$ | ${\cal A}_{FB}$
---|---|---|---
BABAR [17] | 0.1-6.25 | $0.35\pm 0.16\pm 0.04$ | $0.24^{+0.18}_{-0.23}\pm 0.05$
Belle [7] | 1-6 | $0.67\pm 0.23\pm 0.04$ | $0.26^{+0.27}_{-0.30}\pm 0.07$
CDF [8] | 1-6 | $0.5^{+0.27}_{-0.30}\pm 0.04$ | $0.43^{+0.36}_{-0.37}\pm 0.06$
SM [24] | 1-6 | $0.73^{+0.13}_{-0.23}$ | $-0.05^{+0.03}_{-0.04}$
Table 2: BABAR, Belle, and CDF measurements of ${\cal F}_{L}$ and ${\cal
A}_{FB}$ from $B\rightarrow K^{*}\ell^{+}\ell^{-}$ modes in the low $q^{2}$
region.

Figure 2: (left) Measurements of ${\cal F}_{L}$ and (right) Measurements of
${\cal A}_{FB}$ in $B\rightarrow K^{(*)}\ell^{+}\ell^{-}$ modes by BABAR
(black squares), Belle ( brown dots) and CDF (green triangles). The SM
prediction (flipped-sign $C^{eff}_{7}$ model) is shown by the upper red (lower
blue) curve for ${\cal F}_{L}$ and the lower red (upper blue) curve for ${\cal
A}_{FB}$.

##  4 Search for $B^{+}\rightarrow K^{+}\tau^{+}\tau^{-}$

In the SM, the $q^{2}$ dependence of the $B\rightarrow X_{s}\tau^{+}\tau^{-}$
decay rate has a shape similar to that of $B\rightarrow X_{s}\mu^{+}\mu^{-}$
in the high $q^{2}$ region. The $B^{+}\rightarrow K^{+}\tau^{+}\tau^{-}$
branching fraction is predicted to be $\sim 2\times 10^{-7}$ in the SM, which
is $50-60\%$ of the total inclusive branching fraction [21]. Enhancements are
predicted in models beyond the SM. In the next-to-minimal supersymmetric
models (NMSSM), for example, the rate may be enhanced by the squared tau-to-
muon mass ratio $(m_{\tau}/m_{\mu})^{2}\sim 280$. Since signal final states
contain 2-4$\nu$, a different analysis strategy is needed here to control
backgrounds.

BABAR has performed the first search for $B^{+}\rightarrow
K^{+}\tau^{+}\tau^{-}$ using an integrated luminosity of $423~{}\rm fb^{-1}$
which corresponds to 465 $B\overline{B}$ events. The recoiling (”tag”) $B$ is
reconstructed in many hadronic final states, $B^{-}\rightarrow D^{(*)0,+}X$,
where $X$ represents up to six hadrons
($\pi^{\pm},\pi^{0},K^{\pm},K^{0}_{S}$). Using $m_{ES}$ and $\Delta E$ the tag
is selected with an efficiency of $\sim 0.2\%$. The single-prong $\tau$ decays
$\tau\rightarrow e\nu\overline{\nu},\tau\rightarrow\mu\nu\overline{\nu}$ and
$\tau\rightarrow\pi\nu$ are selected as signal modes. Thus, signal candidates
are required to have only three charged particles of which one is an
identified kaon with charge opposite to the tag $B$ and $0.44<p_{K}<1.4~{}\rm
GeV/c$ in the center-of-mass frame. The two remaining particles must have
opposite charge, be consistent with the signal $\tau$ decays, have
$p<1.59~{}\rm GeV/c$ and a mass $M_{pair}<2.89~{}\rm GeV/c^{2}$. Further
requirements are $q^{2}=(\vec{p}_{\mathchar
28935\relax(4S)}-\vec{p}_{tag}-\vec{p}_{K})^{2}/c^{2}>14.23~{}\rm
GeV^{2}/c^{4}$, a missing energy ($i.e.$ the energy carried off by neutrinos
estimated as the difference between $\mathchar 28935\relax(4S)$ energy and
that of all observed particles) of $1.39<E_{miss}<3.38~{}\rm GeV$, and neutral
energy deposited in the electromagnetic calorimeter $E_{extra}<0.74~{}\rm
GeV$. Continuum background is suppressed by $|\cos\theta_{T}|<0.8$, where
$\theta_{T}$ is the opening angle between the thrust axis of the tag and that
of the rest of the event. The largest remaining background originates from
$B^{+}\rightarrow D^{0}X^{+}$, which is suppressed by combining the signal
$K^{+}$ with the $\tau$ daughter of opposite charge assigned the $\pi$ mass
hypothesis and requiring a mass $M_{K\pi}>1.96~{}\rm GeV/c^{2}$.

BABAR observes 47 events with an expected background of $64.7\pm 7.3$ events.
Including systematic uncertainties a branching fraction upper limit of ${\cal
B}(B\rightarrow K^{+}\tau^{+}\tau^{-})<3.3\times 10^{-3}$ is set at $90\%$
confidence level (CL).

##  5 Conclusion

BABAR and Belle have measured branching fractions, rate asymmetries and
angular observables in $B\rightarrow K^{(*)}\ell^{+}\ell^{-}$ final states.
Recently, CDF contributed new measurements on branching fractions and angular
observables in $B\rightarrow K^{(*)}\mu^{+}\mu^{-}$. Except for the isospin
asymmetry at low values of $q^{2}$ all other measurements are consistent with
the SM, though ${\cal F}_{L}$ and ${\cal A}_{FB}$ agree also with the flipped-
sign $C^{eff}_{7}$ model. BABAR has performed the first search for
$B^{+}\rightarrow K^{+}\tau^{+}\tau^{-}$ setting a branching fraction upper
limit of ${\cal B}(B^{+}\rightarrow K^{+}\tau^{+}\tau^{-})<3.3\times 10^{-3}$
at $90\%~{}CL$. Although all experiments are expected to update results with
the final data sets, significant improvement in precision will come from LHCb
and the Super B-factories. In these new experiments, sufficiently large data
samples will be collected to measure the full angular distribution from which
the 12 observables $J_{i}$ [15] can be measured with high precision in
different bins of $q^{2}$. In turn, the Wilson coefficients can be determined
with high precision to reveal small discrepancies with respect to the SM
predictions [3, 23].

ACKNOWLEDGEMENTS

I would like to thank my BABAR colleague K. Flood for useful discussions. This
work has been supported by the Norwegian Research Council.

## References

  * [1] G. Buchalla, A. J. Buras and M. E. Lautenbacher, Rev. Mod. Phys. 68, 1125 (1996); C. Bobeth, M. Misiak and J. Urban, Nucl. Phys. B574, 291 (2000); H.H Asatryan $et~{}al.$, Phys. Rev. D65, 034009 (2002); Phys. Lett. B507, 162, (2001); G. Hiller and F.Krüger, Phys.Rev. D69, 074020 (2004); M. Beneke, Th. Feldmann, and D. Seidel; Nucl. Phys.B612, 25 (2001);M. Beneke, Th. Feldmann, and D. Seidel; Eur.Phys.J. C41, 173 (2005).
  * [2] G. Burdman, Phys. Rev. D52, 6400 (1995); J. L. Hewett and J. D. Wells, Phys. Rev. D55, 5549 (1997); W. J. Li, Y. B. Dai and C. S. Huang, Eur. Phys. J. C40, 565 (2005); Y. G. Xu, R. M. Wang and Y. D. Yang, Phys. Rev. D74, 114019 (2006); P. Colangelo et al., Phys. Rev. D73, 115006 (2006); C.-H. Chen and C.Q. Geng, Phys. Rev. D 66 094018 (2002);C. Bobeth et al, Phys. Rev. D64 074014 (2001).
  * [3] K.S.M. Lee et al., Phys. Rev. D75, 034016 (2007).
  * [4] G. Isidori, Y. Nir, G. Prerez, arXiv:1002.0900 (2010).
  * [5] A. Ali, E. Lunghi, C. Greub and G. Hiller, Phys. Rev. D 66, 034002 (2002).
  * [6] B. Aubert et al. (BABAR collaboration), Phys. Rev. Lett.102, 091803 (2009).
  * [7] J.T. Wei et al. (Belle collaboration), Phys. Rev. Lett.103, 171801 (2009).
  * [8] T. Aaltonen et al. (CDF collaboration), CDF note 10047 (2010).
  * [9] B. Aubert et al. (BABAR collaboration), Phys. Rev. Lett.93, 081862 (2004).
  * [10] C.C.Chiang (Belle collaboration), talk at ICHEP10 (2010).
  * [11] F. Krüger, L. M. Sehgal, N. Sinha and R. Sinha, Phys. Rev. D61, 114028 (2000), [Erratum-ibid. D63, 019901 (2001)].
  * [12] T. Feldmann and J. Matias, JHEP 0301, 074 (2003).
  * [13] Q. S. Yan, C. S. Huang, W. Liao and S. H. Zhu, Phys. Rev. D 62, 094023 (2000).
  * [14] C. Bobeth, G. Hiller and G. Piranishvili, JHEP 0807, 106 (2008).
  * [15] F. Krüger et al., Phys. Rev. D61, 114028 (2000); Erratum-ibid D63, 019901 (2001).
  * [16] C.S. Kim et al., Phys. Rev. D 62, 034013 (2000).
  * [17] B. Aubert et al. (BABAR collaboration), Phys. Rev. D79, 031102 (2009).
  * [18] G. Buchalla $et~{}al.$, Phys. Rev. D63, 014015 (2001).
  * [19] G. Buchalla $et~{}al.$, Phys. Rev. D63, 014015 (2001).
  * [20] A. Hovhannisyan, W. S. Hou and N. Mahajan, Phys. Rev. D 77, 014016 (2008).
  * [21] J.L. Hewett, Phys. Rev. D53, 4964 (1995).
  * [22] K. Flood, talk at the Int. Conf. on HEP, Paris July 22-28 (2010).
  * [23] F. Krüger and J. Matias, Phys. Rev. D71, 094009 (2005).
  * [24] C. Bobeth, G. Hiller and D. van Dyk, JHEP 1007, 098 (2010).