In mixed traffic, one of the challenges for autonomous driving technology is how to safe and socially acceptable interaction with human-driven vehicles (HVs). Understanding human cognitive processes during decision-making in interactions with other road users is crucial for enhancing the smooth execution of driving tasks by autonomous vehicles (AVs). This paper proposes a cognitive model of the driver's cumulative information processing based on drift-diffusion model (DDM). By incorporating the initial decision biases, drift rate, and boundary (depending on the initial speed and gaps between ego vehicle and surrounding users) into the existing DDM, our model captures the integrated interaction between individual drivers and other road users. Classic emergency collision avoidance scenarios were constructed based on a driving simulation platform. Our cognitive model accurately described human decision-making in high-risk scenarios, identified key qualitative and quantitative input variables affecting the driver's cognitive processes, and quantified the safety thresholds of the driver's cumulative information processing. Results can support the personalized modeling of human drivers' cognition and facilitate safe and effective interactions between HVs and AVs.

Urged by a close future perspective of a traffic flow made of a mix of human-driven vehicles and automated vehicles (AVs), research has recently focused on studying the traffic flow characteristics of Adaptive Cruise Controls (ACCs), the most typical AV. However, in most works, the ACC system is studied under a simplifying and unrealistic assumption, or the ACC system modeled is inaccurate. This paper proposes a general hierarchical control system to model ACC systems with several assumptions based on the deficiencies above. Moreover, a field experiment was conducted, and the corresponding experimental data was used to verify the proposed hierarchical control system and assumptions. In addition, string stability is explored along with sensitivity analyses of control parameters based on an example under the constant time gap policy. The results show that different upper-level controller parameters have different delays, where the delay of the speed is negligible; the introduction of actuator delay and lag in the lower-level controller can significantly improve the model goodness of fit. Furthermore, optimizing the delay and lag in the lower-level controller can significantly enhance the string stability of ACCs than optimizing the control parameters.

This chapter summarizes the presentations of speakers addressing such issues during the Automated Vehicles Symposium 2020 (AVS20) held virtually on July 27–30, 2020. These speakers participated in the break-out session titled “Artificial Intelligence for Automated Vehicle Control and Traffic Operations: Challenges and Opportunities”. The corresponding discussion and recommendations are presented in terms of the lessons learned and the future research directions to be adopted to benefit from AI in order to develop safer and more efficient connected and automated vehicles (CAV). This session was organized by the Transportation Research Board (TRB) Committee on Traffic Flow Theory and Characteristics (ACP50) and the TRB Committee on Artificial Intelligence and Advanced Computing Applications (AED50).