Drivers’ trust in an autonomous system

2016 ◽  
Vol 60 (1) ◽  
pp. 1334-1338 ◽  
Author(s):  
Timothy J. Wright ◽  
William J. Horrey ◽  
Mary F. Lesch ◽  
Md Mahmudur Rahman
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous System ◽  
Driving Simulator ◽  
Body Movement ◽  
Autonomous Driving ◽  
Behavioral Measure ◽  
Body Movements ◽  
Trust In Automation ◽  
Future Work ◽  
Autonomous Driving System

Drivers’ trust in automation will likely determine the extent that autonomous and semi-autonomous vehicles are adopted, and once adopted, used properly. Unfortunately, previous studies have typically utilized overt subjective measures in determining an individual’s level of trust in automation. The current study aims to evaluate a covert behavioral measure of trust based on drivers’ body (head, hand, and foot) movements as they experience a simulated autonomous system. Videos of drivers interacting with an autonomous driving system in a driving simulator were coded. Body movement counts and average durations were derived from this coding and these data were compared across quartile rankings (based on self-reported trust) to examine body movements’ sensitivity to drivers’ level of trust. Results suggest body movements are not sensitive to individual differences in reported trust. Future work should further examine the utility of this covert behavioral metric by further examining situational differences.

scholarly journals Switching from autopilot to the driver: A transient performance analysis

2019 ◽  
Vol 234 (5) ◽  
pp. 1346-1360 ◽  
Author(s):  
Seshan Ramanathan Venkita ◽  
Dehlia Willemsen ◽  
Mohsen Alirezaei ◽  
Henk Nijmeijer
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous System ◽  
A Priori ◽  
Autonomous Driving ◽  
Special Focus ◽  
Modal Control ◽  
Driving System ◽  
Lateral Dynamics ◽  
Human Driver ◽  
Autonomous Driving System

One of the main safety concerns associated with semi-autonomous vehicles is the sharing of control between a human driver and an autonomous driving system. Even with an attentive driver, such switches in control may pose a threat to the safety of the driver and the surrounding vehicles. The aim of this study is to develop an indicator that can measure the level of safety during a driver take-over, using knowledge about the system known a priori. A model-based approach is used to analyse the system with special focus on the lateral dynamics of the vehicle. The driver and the vehicle are modelled as linear systems, and a path tracking controller is used to serve as an autonomous system. With this structure, shared control is studied as a switched system, in which the vehicle’s lateral control switches between the autonomous system and the driver. A bound on the transient dynamics that arise due to a switch is derived, using the induced [Formula: see text] norm. This bound is then used to formulate an indicator that checks if the states/outputs of interest are within acceptable limits. A comparison with simulation results has shown that the indicator successfully captures the effect of different system parameters on take-over safety, although in a slightly conservative manner. This indicator can be further developed as a tool to be used in the design and evaluation of shared-/multi-modal control systems in future vehicles.

scholarly journals A Personalized Behavior Learning System for Human-Like Longitudinal Speed Control of Autonomous Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3672 ◽  
Author(s):  
Chao Lu ◽  
Jianwei Gong ◽  
Chen Lv ◽  
Xin Chen ◽  
Dongpu Cao ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Speed Control ◽  
Autonomous Driving ◽  
Learning System ◽  
Superior Performance ◽  
Level Control ◽  
Cruise Control ◽  
Behavior Learning ◽  
Main Component

As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and behaviors of different drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in this paper to improve the performance of the traditional motion planner. This system is based on the neural reinforcement learning (NRL) technique, which can learn from human drivers online based on the on-board sensing information and realize human-like longitudinal speed control (LSC) through the learning from demonstration (LFD) paradigm. Under the LFD framework, the desired speed of human drivers can be learned by PBLS and converted to the low-level control commands by a proportion integration differentiation (PID) controller. Experiments using driving simulator and real driving data show that PBLS can adapt to different drivers by reproducing their driving behaviors for LSC in different scenes. Moreover, through a comparative experiment with the traditional adaptive cruise control (ACC) system, the proposed PBLS demonstrates a superior performance in maintaining driving comfort and smoothness.

scholarly journals Driver Take-Over Reaction in Autonomous Vehicles with Rotatable Seats

Safety ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 34
Author(s):  
Shi Cao ◽  
Pinyan Tang ◽  
Xu Sun
Keyword(s):  
Reaction Time ◽  
Interior Design ◽  
Situation Awareness ◽  
Repeated Measures ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Maximum Magnitude ◽  
Significant Difference ◽  
Driving Conditions

A new concept in the interior design of autonomous vehicles is rotatable or swivelling seats that allow people sitting in the front row to rotate their seats and face backwards. In the current study, we used a take-over request task conducted in a fixed-based driving simulator to compare two conditions, driver front-facing and rear-facing. Thirty-six adult drivers participated in the experiment using a within-subject design with take-over time budget varied. Take-over reaction time, remaining action time, crash, situation awareness and trust in automation were measured. Repeated measures ANOVA and Generalized Linear Mixed Model were conducted to analyze the results. The results showed that the rear-facing configuration led to longer take-over reaction time (on average 1.56 s longer than front-facing, p < 0.001), but it caused drivers to intervene faster after they turned back their seat in comparison to the traditional front-facing configuration. Situation awareness in both front-facing and rear-facing autonomous driving conditions were significantly lower (p < 0.001) than the manual driving condition, but there was no significant difference between the two autonomous driving conditions (p = 1.000). There was no significant difference of automation trust between front-facing and rear-facing conditions (p = 0.166). The current study showed that in a fixed-based simulator representing a conditionally autonomous car, when using the rear-facing driver seat configuration (where participants rotated the seat by themselves), participants had longer take-over reaction time overall due to physical turning, but they intervened faster after they turned back their seat for take-over response in comparison to the traditional front-facing seat configuration. This behavioral change might be at the cost of reduced take-over response quality. Crash rate was not significantly different in the current laboratory study (overall the average rate of crash was 11%). A limitation of the current study is that the driving simulator does not support other measures of take-over request (TOR) quality such as minimal time to collision and maximum magnitude of acceleration. Based on the current study, future studies are needed to further examine the effect of rotatable seat configurations with more detailed analysis of both TOR speed and quality measures as well as in real world driving conditions for better understanding of their safety implications.

Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator

2015 ◽  
Vol 27 (6) ◽  
pp. 660-670 ◽  
Author(s):  
Udara Eshan Manawadu ◽  
◽  
Masaaki Ishikawa ◽  
Mitsuhiro Kamezaki ◽  
Shigeki Sugano ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Driving Experience ◽  
The Road ◽  
Traffic Conditions ◽  
Passenger Vehicles ◽  
New Type ◽  
On The Road ◽  
Near Future

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.

scholarly journals Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2405
Author(s):  
Heung-Gu Lee ◽  
Dong-Hyun Kang ◽  
Deok-Hwan Kim
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Response Speed ◽  
Autonomous Driving ◽  
Human Machine Interface ◽  
Driver Assistance Systems ◽  
Human Machine Interaction ◽  
Driving Assistance ◽  
Assistant Systems ◽  
Machine Interface

Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver’s situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver’s situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.

Pedestrians receptivity in autonomous vehicles: Exploring a video-based assessment

2018 ◽  
Vol 62 (1) ◽  
pp. 2061-2065 ◽  
Author(s):  
Shuchisnigdha Deb ◽  
Christopher R. Hudson ◽  
Daniel W. Carruth ◽  
Darren Frey
Keyword(s):  
Waiting Time ◽  
Autonomous Vehicles ◽  
Walking Speed ◽  
Objective Measures ◽  
Body Movements ◽  
Behavioral Measures ◽  
Stop Sign ◽  
External Feature ◽  
Verbal Message ◽  
Future Work

Pedestrian receptivity toward autonomous vehicles (AVs) usually depends on the extent to which they receive indication of the vehicle’s intended action. Previous studies have typically used overt subjective measures (trust measures, ratings, etc.) and few objective measures (walking speed, waiting time, etc.) to identify external features that can improve pedestrians’ receptivity toward AVs. The current study aims to evaluate pedestrians’ behavioral measures of receptivity based on their body (head and foot) movements as they experience an AV in a virtual traffic environment. Videos of pedestrians at a virtual crosswalk, interacting with an AV that was equipped with an external feature indicating different operator statuses were coded. The operator statuses used in this study included: no driver, attentive driver, and distracted driver. The external features used were: no feature, upraised hand, stop sign, walking silhouette, walk in text, music, and a verbal message. Pedestrian body movements were derived from the video to determine frequency for looking at the approaching vehicle while crossing and stops after initiating crossing. Average durations for initiating crossing after signal were calculated. For no feature condition, the waiting time was calculated when participants observed the car. Data were compared with pedestrians’ self-reported ratings for receptivity to investigate body movements’ sensitivity to participants’ receptivity level. Results suggest body movements are sensitive to individual differences in reported receptivity. Future work should further examine the utility of this behavioral metric by further examining situational differences.

scholarly journals Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Weilong Song ◽  
Guangming Xiong ◽  
Huiyan Chen
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Transition Process ◽  
Autonomous Driving ◽  
Driving Safety ◽  
Time Step ◽  
Reward Function ◽  
Pass Through

Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging problem in an uncontrolled intersection scenario. In order to consider uncertain intentions, we first develop a continuous hidden Markov model to predict both the high-level motion intention (e.g., turn right, turn left, and go straight) and the low level interaction intentions (e.g., yield status for related vehicles). Then a partially observable Markov decision process (POMDP) is built to model the general decision-making framework. Due to the difficulty in solving POMDP, we use proper assumptions and approximations to simplify this problem. A human-like policy generation mechanism is used to generate the possible candidates. Human-driven vehicles’ future motion model is proposed to be applied in state transition process and the intention is updated during each prediction time step. The reward function, which considers the driving safety, traffic laws, time efficiency, and so forth, is designed to calculate the optimal policy. Finally, our method is evaluated in simulation with PreScan software and a driving simulator. The experiments show that our method could lead autonomous vehicle to pass through uncontrolled intersections safely and efficiently.

Autonomous Driving

2017 ◽  
Vol 9 (2) ◽  
pp. 58-74 ◽  
Author(s):  
Marcel Walch ◽  
Kristin Mühl ◽  
Martin Baumann ◽  
Michael Weber
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Time Budget ◽  
Autonomous Driving ◽  
User Preferences ◽  
Automated Systems ◽  
Simulator Study ◽  
Short Time ◽  
Safe Performance ◽  
Positive Effect

Autonomous vehicles will need de-escalation strategies to compensate when reaching system limitations. Car-driver handovers can be considered one possible method to deal with system boundaries. The authors suggest a bimodal (auditory and visual) handover assistant based on user preferences and design principles for automated systems. They conducted a driving simulator study with 30 participants to investigate the take-over performance of drivers. In particular, the authors examined the effect of different warning conditions (take-over request only with 4 and 6 seconds time budget vs. an additional pre-cue, which states why the take-over request will follow) in different hazardous situations. Their results indicated that all warning conditions were feasible in all situations, although the short time budget (4 seconds) was rather challenging and led to a less safe performance. An alert ahead of a take-over request had the positive effect that the participants took over and intervened earlier in relation to the appearance of the take-over request. Overall, the authors' evaluation showed that bimodal warnings composed of textual and iconographic visual displays accompanied by alerting jingles and spoken messages are a promising approach to alert drivers and to ask them to take over.

Challenges and Requirements of Immersive Media in Autonomous Car: Exploring the Feasibility of Virtual Entertainment Applications

i-com ◽  
2019 ◽  
Vol 18 (2) ◽  
pp. 105-125
Author(s):  
Carolin Wienrich ◽  
Kristina Schindler
Keyword(s):  
User Experience ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Simulator Sickness ◽  
Significant Difference ◽  
Driving Scenario ◽  
Immersive Media

Abstract This paper investigated the influence of VR-entertainment systems on passenger and entertainment experience in vehicles with smooth movements. To simulate an autonomous driving scenario, a tablet and a mobile VR-HMD were evaluated in a dynamic driving simulator. Passenger, user and entertainment experience were measured through questionnaires based on comfort/discomfort, application perception, presence, and simulator sickness. In two experiments, two film sequences with varying formats (2D versus 3D) were presented. In Experiment 1, the established entertainment system (tablet + 2D) was tested against a possible future one (HMD + 3D). The results indicated a significantly more favorable experience for the VR-HMD application in the dimensions of user experience (UX) and presence, as well as low simulator sickness values. In Experiment 2, the film format was held constant (2D), and only the device (tablet versus HMD) was varied. There was a significant difference in all constructs, which points to a positive reception of the HMD. Additional analyses of the HMD device data for both experiments showed that the device and not the film format contributed to the favorable experience with the HMD. Additionally, the framework to evaluate the new application context of VR as an entertainment system in autonomous vehicles was discussed.

Autonomy and Responsibility in Hybrid Systems

2017 ◽  
Author(s):  
Wulf Loh ◽  
Janina Loh
Keyword(s):  
Hybrid Systems ◽  
Hybrid System ◽  
Moral Dilemma ◽  
Autonomous Driving ◽  
Shared Responsibility ◽  
Driving System ◽  
Autonomous Cars ◽  
Self Driving Cars ◽  
Trolley Cases ◽  
Autonomous Driving System

In this chapter, we give a brief overview of the traditional notion of responsibility and introduce a concept of distributed responsibility within a responsibility network of engineers, driver, and autonomous driving system. In order to evaluate this concept, we explore the notion of man–machine hybrid systems with regard to self-driving cars and conclude that the unit comprising the car and the operator/driver consists of such a hybrid system that can assume a shared responsibility different from the responsibility of other actors in the responsibility network. Discussing certain moral dilemma situations that are structured much like trolley cases, we deduce that as long as there is something like a driver in autonomous cars as part of the hybrid system, she will have to bear the responsibility for making the morally relevant decisions that are not covered by traffic rules.

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