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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. Autonomous Driving Security: A Comprehensive Threat Model of Attacks and Mitigation Strategies

   Hoque, Mohammad Aminul; Hasan, Ragib (July 2022, Proceedings of the 8th IEEE World Forum on Internet of Things (WF-IOT))

   Autonomous vehicles (AVs) are envisioned to enhance safety and efficiency on the road, increase productivity, and positively impact the urban transportation system. Due to recent developments in autonomous driving (AD) technology, AVs have started moving on the road. However, this promising technology has many unique security challenges that have the potential to cause traffic accidents. Though some researchers have exploited and addressed specific security issues in AD, there is a lack of a systematic approach to designing security solutions using a comprehensive threat model. A threat model analyzes and identifies potential threats and vulnerabilities. It also identifies the attacker model and proposes mitigation strategies based on known security solutions. As an emerging cyber-physical system, the AD system requires a well-designed threat model to understand the security threats and design solutions. This paper explores security issues in the AD system and analyzes the threat model using the STRIDE threat modeling process. We posit that our threat model-based analysis will help improve AVs' security and guide researchers toward developing secure AVs. 

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3. High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

   https://doi.org/10.3390/s21020464  

   Ma, Wei; Qian, Sean (January 2021, Sensors)

   null (Ed.)

   Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management. 

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4. Establishing trust in vehicle-to-vehicle coordination: a sensor fusion approach

   https://doi.org/10.1145/3508396.3517075  

   Veselsky, Jakob; West, Jack; Ahlgren, Isaac; Thiruvathukal, George K.; Klingensmith, Neil; Goel, Abhinav; Jiang, Wenxin; Davis, James C.; Lee, Kyuin; Kim, Younghyun (March 2022, Proceedings of the 23rd Annual International Workshop on Mobile Computing Systems and Applications)

   As we add more autonomous and semi-autonomous vehicles (AVs) to our roads, their effects on passenger and pedestrian safety are becoming more important. Despite extensive testing before deployment, AV systems are not perfect at identifying hazards in the roadway. Although a particular AV’s sensors and software may not be 100% accurate at identifying hazards, there is an untapped pool of information held by other AVs in the vicinity that could be used to quickly and accurately identify roadway hazards before they present a safety threat. 

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5. The Impact of Cybersecurity Attacks on Human Trust in Autonomous Vehicle Operations

   https://doi.org/10.1177/00187208241283321  

   Lim, Cherin; Prendez, David; Boyle, Linda Ng; Rajivan, Prashanth (May 2025, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Objective This study examines the extent to which cybersecurity attacks on autonomous vehicles (AVs) affect human trust dynamics and driver behavior. Background Human trust is critical for the adoption and continued use of AVs. A pressing concern in this context is the persistent threat of cyberattacks, which pose a formidable threat to the secure operations of AVs and consequently, human trust. Method A driving simulator experiment was conducted with 40 participants who were randomly assigned to one of two groups: (1) Experience and Feedback and (2) Experience-Only. All participants experienced three drives: Baseline, Attack, and Post-Attack Drive. The Attack Drive prevented participants from properly operating the vehicle in multiple incidences. Only the Experience and Feedback group received a security update in the Post-Attack Drive, which was related to the mitigation of the vehicle’s vulnerability. Trust and foot positions were recorded for each drive. Results Findings suggest that attacks on AVs significantly degrade human trust, and that trust remains degraded even after an error-free drive. Providing an update about the mitigation of the vulnerability did not significantly affect trust repair. Conclusion Trust toward AVs should be analyzed as an emergent and dynamic construct that requires autonomous systems capable of calibrating trust after malicious attacks through appropriate experience and interaction design. Application The results of this study can be applied when building driver- and situation-adaptive AI systems within AVs. 

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