scholarly journals Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jaehyun Jason So ◽  
Sungho Park ◽  
Jonghwa Kim ◽  
Jejin Park ◽  
Ilsoo Yun
Keyword(s):  
Driving Simulator ◽  
Road Traffic ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle System ◽  
Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

scholarly journals Analysis of the advancements in real-life performance of highly automated vehicles' with regard to the road traffic safety

2018 ◽  
Vol 231 ◽  
pp. 05003 ◽  
Author(s):  
Arkadiusz Matysiak ◽  
Paula Razin
Keyword(s):  
Traffic Safety ◽  
Large Scale ◽  
Road Traffic ◽  
Real Life ◽  
Automated Vehicles ◽  
Automated Driving ◽  
The Road ◽  
Road Traffic Safety ◽  
On The Road ◽  
The Impact

The article presents the analysis of the performance of the vehicles equipped with automated driving systems (ADS) which were tested in real-life road conditions from 2015 to 2017 in the state of California. It aims at the effort to assess the impact on the road safety the continuous technological advancements in driving automation might have, based on of the first large-scale, real-life test deployments. Vehicle manufacturers and other stakeholders testing the highly automated vehicles in California are obliged to issue yearly reports which provide an insight on the test scale as well as the technology maturity. The so-called 'disengagement reports' highlight the range and number of control takeovers between the ADS and driver, which are made either based on driver's decision or information provided by the vehicle itself. The analysis of these reports allowed to investigate the development made in automated driving technology throughout the years of tests, as well as the direct or indirect influence of the external factors (e.g. various weather conditions) on the ADS performance. The results show that there is still a significant gap in reliability and safety between human drivers and highly automated vehicles which has been yet steadily decreasing due to technology advancements made while driving in the specific infrastructure and traffic conditions of California.

Assessing Drivers’ Trust of Automated Vehicle Driving Styles With a Two-Part Mixed Model of Intervention Tendency and Magnitude

2019 ◽  
pp. 001872081988036 ◽  
Author(s):  
John D. Lee ◽  
Shu-Yuan Liu ◽  
Joshua Domeyer ◽  
Azadeh DinparastDjadid
Keyword(s):  
Mixed Model ◽  
Driving Simulator ◽  
Statistical Technique ◽  
Brake Pedal ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Accelerator Pedal ◽  
Driving Style ◽  
Automated Vehicle ◽  
Stop Sign

Objective: This study examines how driving styles of fully automated vehicles affect drivers’ trust using a statistical technique—the two-part mixed model—that considers the frequency and magnitude of drivers’ interventions. Background: Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle’s driving style might have an important influence. Method: A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation’s driving style and the person’s driving style affected the frequency and magnitude of their pedal depression. Results: The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed. Conclusion: Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers’ trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator. Application: We offer a measure and method for assessing driving styles.

scholarly journals Validation of a driving simulator for research into human factors issues of automated vehicles

2019 ◽  
Vol 30 (2) ◽  
pp. 37-44
Author(s):  
Nebojsa Tomasevic ◽  
Tim Horberry ◽  
Brian Fildes
Keyword(s):  
Statistical Analysis ◽  
Human Factors ◽  
Driving Simulator ◽  
Research Centre ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Simulated Driving ◽  
Automated Vehicle ◽  
Perception Of Safety ◽  
Accident Research

This study evaluated the behavioural validity of the Monash University Accident Research Centre automation driving simulator for research into the human factors issues associated with automated driving. The study involved both on-road and simulated driving. Twenty participants gave ratings of their willingness to resume control of an automated vehicle and perception of safety for a variety of situations along the drives. Each situation was individually categorised and ratings were processed. Statistical analysis of the ratings confirmed the behavioural validity of the simulator, in terms of the similarity of the on-road and simulator data.

scholarly journals Underload on the Road: Measuring Vigilance Decrements During Partially Automated Driving

2021 ◽  
Vol 12 ◽  
Author(s):  
Thomas McWilliams ◽  
Nathan Ward
Keyword(s):  
Cognitive Demand ◽  
Future Research ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Critical Situation ◽  
Vehicle Technology ◽  
On The Road

Partially automated vehicle technology is increasingly common on-road. While this technology can provide safety benefits to drivers, it also introduces new concerns about driver attention. In particular, during partially automated driving (PAD), drivers are expected to stay vigilant so they can readily respond to important events in their environment. However, using partially automated vehicles on the highway places drivers in monotonous situations and requires them to do very little. This can place the driver in a state of cognitive underload in which they experience a very small amount of cognitive demand. In this situation, drivers can exhibit vigilance decrements which impact their ability to respond to on-road threats. This is of particular concern in situations when the partially automated vehicle fails to respond to a potentially critical situation and leaves all responsibility to safely navigate to the driver. This paper reviews situations that lead to vigilance decrements and characterizes the different methodologies of measuring driver vigilance during PAD, highlighting their advantages and limitations. Based on our reading of the literature, we summarize several factors future research on vigilance decrements in PAD should consider.

scholarly journals Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Johannes Ossig ◽  
Stephanie Cramer ◽  
Klaus Bengler
Keyword(s):  
Driving Behavior ◽  
Automation System ◽  
Automated Driving ◽  
Driving Style ◽  
Driving Experience ◽  
Automated Vehicle ◽  
Future Design ◽  
Vehicle System ◽  
The Future ◽  
Conceptual Distinction

In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving.

Analysis of Driver's Characteristics Based on Internet of Vehicles

2014 ◽  
Vol 505-506 ◽  
pp. 1148-1152
Author(s):  
Jian Qun Wang ◽  
Xiao Qing Xue ◽  
Ning Cao
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Classification Model ◽  
Economic Losses ◽  
Lane Changing ◽  
Internet Of Vehicles ◽  
Factors Affecting ◽  
The Road ◽  
The University ◽  
Driver’S Characteristics

The road traffic accidents caused huge economic losses and casualties, so it had been focused by the researchers. Lane changing characteristic is the most relevant characteristic with safety. The intent of lane changing was discussed. Firstly, the factors affecting the intent were analyzed, the speed satisfaction value and the space satisfaction value were proposed; then the data from the University of California, Berkeley was extracted and the number of vehicles changed lane more often and the vehicle ID were obtained; the BP neural network classification model was established, it was trained and testified by actual data. The results shown the method could predict the intent accurately.

scholarly journals Behavioral Changes to Repeated Takeovers in Highly Automated Driving: Effects of the Takeover-Request Design and the Nondriving-Related Task Modality

2018 ◽  
Vol 61 (5) ◽  
pp. 839-849 ◽  
Author(s):  
Fabienne Roche ◽  
Anna Somieski ◽  
Stefan Brandenburg
Keyword(s):  
Subjective Experience ◽  
Vehicle Control ◽  
Behavioral Changes ◽  
Behavior Changes ◽  
Automated Driving ◽  
The Road ◽  
Gaze Patterns ◽  
On The Road ◽  
Task Modality ◽  
Highly Automated Driving

Objective: We investigated drivers’ behavior and subjective experience when repeatedly taking over their vehicles’ control depending on the design of the takeover request (TOR) and the modality of the nondriving-related task (NDRT). Background: Previous research has shown that taking over vehicle control after highly automated driving provides several problems for drivers. There is evidence that the TOR design and the NDRT modality may influence takeover behavior and that driver behavior changes with more experience. Method: Forty participants were requested to resume control of their simulated vehicle six times. The TOR design (auditory or visual-auditory) and the NDRT modality (auditory or visual) were varied. Drivers’ takeover behavior, gaze patterns, and subjective workload were recorded and analyzed. Results: Results suggest that drivers change their behavior to the repeated experience of takeover situations. An auditory TOR leads to safer takeover behavior than a visual-auditory TOR. And with an auditory TOR, the takeover behavior improves with experience. Engaging in the visually demanding NDRT leads to fewer gazes on the road than the auditory NDRT. Participants’ fixation duration on the road decreased over the three takeovers with the visually demanding NDRT. Conclusions: The results imply that (a) drivers change their behavior to repeated takeovers, (b) auditory TOR designs might be preferable over visual-auditory TOR designs, and (c) auditory demanding NDRTs allow drivers to focus more on the driving scene. Application: The results of the present study can be used to design TORs and determine allowed NDRTs in highly automated driving.

scholarly journals Experimental Study on Different Types of Curves for Ride Comfort in Automated Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Naohisa Hashimoto ◽  
Yusuke Takinami ◽  
Makoto Yamamoto
Keyword(s):  
Traffic Accidents ◽  
Field Experiments ◽  
Ride Comfort ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Significance Level ◽  
Automated Vehicle ◽  
Driving Assistance ◽  
Human Factor Research ◽  
Automated Vehicle Control

Vehicle automation is among the best possible solutions for traffic issues, including traffic accidents, traffic jams, and energy consumption. However, the user acceptance of automated vehicles is critical and is affected by riding comfort. In addition, human factors in automated vehicle control should be clear. This study evaluates the effect of different courses on driving comfort in automated vehicles using field experiments with 25 subjects. This study focused on lateral motion, but speed control was not targeted. Further, generating a path for obstacle avoidance and lane keeping, which have several constraining conditions, was also not targeted. Rendering a comfortable path is beneficial for developing an acceptable system as a car developer and for building new curves for automated or driving assistance systems from the perspective of construction. The automated vehicle drove at a speed of 30 km/h on four courses, namely, clothoid, two types of spline curves, and arc, based on the real intersection. Each participant sat on both the driver and passenger seat and answered a questionnaire. The experimental data indicated the clothoid course to be the most comfortable, while the arc was most uncomfortable for a significance level of 1%. These tendencies are applicable to driver and passenger seats, all genders, and experiences and will be beneficial for human factor research in automated vehicle control.

Necessity of Automated Vehicle Control Customization: Experimental Results during Lane Changing

2018 ◽  
Vol 2672 (22) ◽  
pp. 1-9
Author(s):  
Naohisa Hashimoto ◽  
Simon Thompson ◽  
Shin Kato ◽  
Ali Boyali ◽  
Sadayuki Tsugawa
Keyword(s):  
Vehicle Control ◽  
Experimental Results ◽  
Single Type ◽  
Automated Driving ◽  
Lane Changing ◽  
Automated Vehicle ◽  
Vehicle Trajectories ◽  
Significant Difference ◽  
The Subject ◽  
Automated Vehicle Control

This study investigated the necessity of automated vehicle control customization for individual drivers via a lane-changing experiment involving 35 subjects and an automated minivan. The experiment consisted of two automated driving conditions: one in which the subject was unable to override vehicle controls, the other with the option to override when the subject felt it was necessary. The automated vehicle drove at a speed of 40 km/h along three kinds of planned paths for lane changing, generated by Bezier curves; the distance required for lane changing was varied to obtain the preferred path of each subject. Various data obtained during driving, including vehicle trajectories and steering angles produced by subjects were logged. After automated driving, a questionnaire was administered to each subject. The experimental data showed that there was a statistically significant difference between comfort when the vehicle drove along the subject’s preferred path, and when it drove along other paths. The results of the questionnaire indicated that 46% of the subjects preferred the planned path that most closely resembled their own. In addition, quantitative analysis of driving data found that approximately 69% of the subjects preferred an automated driving control that resembled their own. However, it was also observed that certain subjects were open to multiple types of automated vehicle control. The experimental results indicate that drivers will not necessarily accept a single type of automated vehicle control, therefore customization will be necessary to improve acceptance of automated driving.

Cellular Automata–Based Modeling and Simulation of a Mixed Traffic Flow of Manual and Automated Vehicles

2017 ◽  
Vol 2622 (1) ◽  
pp. 105-116 ◽  
Author(s):  
Da Yang ◽  
Xiaoping Qiu ◽  
Lina Ma ◽  
Danhong Wu ◽  
Liling Zhu ◽  
...  
Keyword(s):  
Reaction Time ◽  
Traffic Flow ◽  
Road Traffic ◽  
Flow Characteristics ◽  
Lane Change ◽  
Mixed Traffic ◽  
Automated Vehicles ◽  
Obvious Effect ◽  
Mixed Traffic Flow ◽  
Automated Vehicle

In recent years, automated vehicles have been developing rapidly, and some automated vehicles have begun to drive on highways. The market share of automated vehicles is expected to increase and will greatly affect traffic flow characteristics. This paper focuses on the mixed traffic flow of manual and automated vehicles. The study improves the existing cellular automaton model to capture the differences between manual vehicles and automated vehicles. Computer simulations are employed to analyze the characteristic variations in the mixed traffic flow under different automated vehicle proportions, lane change probabilities, and reaction times. Several new conclusions are drawn in the paper. First, with the increment of the proportion of automated vehicles, freeway capacity increases; the capacity increment is more significant for single-lane traffic than for two-lane traffic. Second, for single-lane traffic flow, reducing the reaction time of the automated vehicle can significantly improve road traffic capacity—as much as doubling it—and reaction time reduction has no obvious effect on the capacity of the two-lane traffic. Third, with the proportion increment of automated vehicles, lane change frequency reduces significantly. Fourth, when the density is 15 < ρ < 55 vehicles/km, the addition of 20% automated vehicles to a traffic flow that consisted of only manual vehicles can decrease congestion by up to 16.7%.

Sign in / Sign up

Export Citation Format

Share Document