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1. Investigating the Effects of Automated Driving Styles and Driver’s Driving Styles on Driver Trust, Acceptance, and Take Over Behaviors

   https://doi.org/10.1177/1071181320641484  

   Ma, Zheng; Zhang, Yiqi (December 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   null (Ed.)

   Autonomous Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, driver could be reluctant to ride with AVs due to the lack of trust and acceptance of AV’s driving styles. The present study investigated the impact of driver’s driving style (aggressive/defensive) and the designed driving styles of AVs (aggressive/defensive) on driver’s trust, acceptance, and take-over behavior in fully autonomous vehicles. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve scenarios in either an aggressive AV or a defensive AV. Results revealed that drivers’ trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV’s driving style and driver’s driving style. The findings implied that driver’s individual differences should be considered in the design of AV’s driving styles to enhance driver’s trust and acceptance of AVs and reduce undesired take over behaviors. 

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2. Analysis of Driver Behavior in Mixed Autonomous and Non-autonomous Traffic Flows

   https://doi.org/10.1177/1071181322661305  

   Liu, Jundi; Boyle, Linda Ng (September 2022, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Autonomous vehicles are expected to improve road safety and efficiency in future transportation systems. A driving simulator study was designed to identify driving styles and the cooperation between human drivers and other AVs. The study captured driver’s following behavior in a fully autonomous driving environment at unsignalized intersections. Participants were asked to make a series of maneuvers (straight through intersection, left turn, and right turn) in two different speed conditions (30, 40 mph) and two different traffic density conditions (with or without other traffic). Analysis of Variance showed that drivers had a significantly larger deviation (defined as the area between two trajectories) during left turn maneuvers when they were traveling at higher speeds. Moreover, the first turning operation had smaller deviation than the second turning operation. The findings have implications for the design of driver-assistance guidance systems in future mixed autonomous and non-autonomous traffic flows. 

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3. Evaluating the driving behavior of autonomous vehicles and human driver vehicles in mixed driver environments at a signalized intersection

   https://doi.org/10.1117/12.2613057  

   Yang, Xinyi; Ahmad, Salman; Nelson, Martha; Rogers, Joshua; Ren, Yihao; Hung, Ying; Lu, Pan (April 2022, Proceedings Volume 12046, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022)

   Zonta, Daniele; Su, Zhongqing; Glisic, Branko (Ed.)

   With the rapid development of smart cities, interest in vehicle automation continues growing. Autonomous vehicles are becoming more and more popular among people and are considered to be the future of ground transportation. Autonomous vehicles, either with adaptive cruise control (ACC) or cooperative adaptive cruise control (CACC), provide many possibilities for smart transportation in a smart city. However, traditional vehicles and autonomous vehicles will have to share the same road systems until autonomous vehicles fully penetrate the market over the next few decades, which leads to conflicts because of the inconsistency of human drivers. In this paper, the performance of autonomous vehicles with ACC/CACC and traditional vehicles in mixed driver environments, at a signalized intersection, were evaluated using the micro-simulator VISSIM. In the simulation, the vehicles controlled by the ACC/CACC and Wiedemann 99 (W99) model represent the behavior of autonomous vehicles and human driver vehicles, respectively. For these two different driver environments, four different transport modes were comprehensively investigated: full light duty cars, full trucks, full motorcycles, and mixed conditions. In addition, ten different seed numbers were applied to each model to avoid coincidence. To evaluate the driving behavior of the human drivers and autonomous vehicles, this paper will compare the total number of stops, average velocity, and vehicle delay of each model at the signalized traffic intersection based on a real road intersection in Minnesota. 

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4. Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharing

   https://doi.org/10.1145/3616398  

   Wang, Jiangwei; Su, Lili; Han, Songyang; Song, Dongjin; Miao, Fei (September 2023, ACM Transactions on Cyber-Physical Systems)

   Hybrid traffic which involves both autonomous and human-driven vehicles would be the norm of the autonomous vehicles’ practice for a while. On the one hand, unlike autonomous vehicles, human-driven vehicles could exhibit sudden abnormal behaviors such as unpredictably switching to dangerous driving modes – putting its neighboring vehicles under risks; such undesired mode switching could arise from numbers of human driver factors, including fatigue, drunkenness, distraction, aggressiveness, etc. On the other hand, modern vehicle-to-vehicle (V2V) communication technologies enable the autonomous vehicles to efficiently and reliably share the scarce run-time information with each other [1]. In this paper, we propose, to the best of our knowledge, the first efficient algorithm that can (1) significantly improve trajectory prediction by effectively fusing the run-time information shared by surrounding autonomous vehicles, and can (2) accurately and quickly detect abnormal human driving mode switches or abnormal driving behavior with formal assurance without hurting human drivers’ privacy. To validate our proposed algorithm, we first evaluate our proposed trajectory predictor on NGSIM and Argoverse datasets and show that our proposed predictor outperforms the baseline methods. Then through extensive experiments on SUMO simulator, we show that our proposed algorithm has great detection performance in both highway and urban traffic. The best performance achieves detection rate of\(97.3\% \), average detection delay of 1.2s, and 0 false alarm. 

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5. Evaluating the mobility performance of autonomous vehicles at a signalized traffic intersection

   https://doi.org/10.1117/12.2606614  

   Ahmad, Salman; Rogers, Joshua; Nelson, Martha; Huang, Ying; Ajmera, Beena; Lu, Pan (April 2022, Proceedings Volume 12046, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022)

   Zonta, Daniele; Su, Zhongqing; Glisic, Branko (Ed.)

   Recent developments in autonomous vehicle (AV) or connected AVs (CAVs) technology have led to predictions that fully self-driven vehicles could completely change the transportation network over the next decades. However, at this stage, AVs and CAVs are still in the development stage which requires various trails in the field and machine learning through autonomous driving miles on real road networks. Until the complete market adoption of autonomous technology, a long transition period of coexistence between conventional and autonomous cars would exist. It is important to study and develop the expected driving behavior of future autonomous cars and the traffic simulation platforms provide an opportunity for researchers and technology developers to implement and assess the different behaviors of self-driving vehicle technology before launching it to the actual ground. This study utilizes PTV VISSIM microsimulation platform to evaluate the mobility performance of unmanned vehicles at a 4-way signalized traffic intersection. The software contains three different AV-ready driving logics such as AV-cautious, AV-normal, and AV-aggressive which were tested against the performance of the conventional vehicles, and the results of the study revealed that the overall network operational performance improves with the progressive introduction of AVs using AV-normal, and AV-aggressive driving behaviors while the AV-cautious driving behavior stays conservative and deteriorates the traffic performance. 

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