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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. DESIGN NEEDS AND PERCEPTIONS OF OLDER ADULTS REGARDING SHARED-USE AUTOMATED VEHICLES

   https://doi.org/10.1093/geroni/igad104.2864  

   Lam, Justin; O’Rourke, Tarryn; Bowman, Caitlin; D’Souza, Clive (December 2023, Innovation in Aging)

   Abstract Many communities struggle to provide safe, accessible, and reliable transportation services for older adults due to high demand, rising costs, driver shortages, and other evolving challenges. Innovative transportation solutions are needed to support the current and future populations of older adults. Low-speed, shared-use, driverless shuttles present an exciting development in automated vehicle (AV) technology with potential to meet mobility needs of older adults in their community. Understanding older adults’ perceptions about and willingness to consider using these emerging modes of transportation is vital to realizing the full potential of these technologies. This presentation summarizes an in-person study conducted with 12 older (average: 66 +/- 4 years of age, range: 60 to 80 years) and 10 younger (average: 44 +/- 11 years) adults that evaluated a stationary, proof-of-concept shared-use AV retrofitted with accessibility features. We will present findings on perceptions regarding accessibility, safety, and willingness to use driverless AVs along with human factors design recommendations. While questionnaire-based studies have been the dominant approach to understanding older adults’ perceptions about shared-use AVs, in-person evaluations even with prototype AVs as described here, provide opportunities to identify goals, needs and preferences of older adults concerning usability and safety in early design stages, and through hands-on exploration help older adults develop good mental models, i.e., understand AV capabilities and limitations, towards building trust and acceptance for these emerging modes of transportation. Research and policy implications will be discussed towards enabling emerging driverless shared-use AV technologies that support safe and independent community mobility for older adults. 

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3. Lights, Camera, Autonomy! Exploring the Opinions of Older Adults Regarding Autonomous Vehicles Through Enactment

   Gluck, A.; Huff, E; Zhang, M; Brinkley, J (January 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Autonomous vehicles (AV), one of the transportation industry’s biggest innovations of the past few decades, bring the promise of safer roads and significantly lower vehicle-related fatalities. While many studies have found largely positive consumer opinions regarding operating and owning such a vehicle, older adults (55+) tend to express concerns about the safety and operational risks of a vehicle with unknown capabilities. To investigate how older adults and AVs may interact, we conducted an improv- style enactment-based participatory design pilot study. We found that initial concerns about trust and safety can be diminished through training and repetitive successful vehicle operation. Additionally, our participants provided insights into the AV design considerations, needs, and interactions for older adults. These findings add to the collective body of autonomous vehicle research by demonstrating that the needs of this growing population, who may benefit significantly from access to AVs, should be considered by manufacturers. 

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4. Continuous integration and testing for autonomous racing software: An experience report from GRAIC

   https://doi.org/10.13140/RG.2.2.28270.33605  

   Jiang, Minghao; Miller, Kristina; Sun, Dawei; Liu, Zexiang; Jia, Yixuan; Datta, Arnab; Ozay, Necmiye; Mitra, Sayan (May 2021, IEEE ICRA 2021, International Conference on Robotics and Automation, Workshop on OPPORTUNITIES AND CHALLENGES WITH AUTONOMOUS RACING)

   Self-driving autonomous vehicles (AVs) have recently gained popularity as a research topic. The safety of AVs is exceptionally important as failure in the design of an AV could lead to catastrophic consequences. AV systems are highly heterogeneous with many different and complex components, so it is difficult to perform end-to-end testing. One solution to this dilemma is to evaluate AVs using simulated racing competition. In this thesis, we present a simulated autonomous racing competition, Generalized RAcing Intelligence Competition (GRAIC). To compete in GRAIC, participants need to submit their controller files which are deployed on a racing ego-vehicle on different race tracks. To evaluate the submitted controller, we also developed a testing pipeline, Autonomous System Operations (AutOps). AutOps is an automated, scalable, and fair testing pipeline developed using software engineering techniques such as continuous integration, containerization, and serverless computing. In order to evaluate the submitted controller in non-trivial circumstances, we populate the race tracks with scenarios, which are pre-defined traffic situations commonly seen in the real road. We present a dynamic scenario testing strategy that generates new scenarios based on results of the ego-vehicle passing through previous scenarios. 

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5. On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles

   https://doi.org/10.1109/CVPR52688.2022.01473  

   Zhang, Qingzhao; Hu, Shengtuo; Sun, Jiachen; Chen, Qi Alfred; Mao, Z. Morley (January 2022, IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR))

   Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe planning and navigation. However, few studies have analyzed the adversarial robustness of trajectory prediction or investigated whether the worst-case prediction can still lead to safe planning. To bridge this gap, we study the adversarial robustness of trajectory prediction models by proposing a new adversarial attack that perturbs normal vehicle trajectories to maximize the prediction error. Our experiments on three models and three datasets show that the adversarial prediction increases the prediction error by more than 150%. Our case studies show that if an adversary drives a vehicle close to the target AV following the adversarial trajectory, the AV may make an inaccurate prediction and even make unsafe driving decisions. We also explore possible mitigation techniques via data augmentation and trajectory smoothing. 

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