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).@en

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.@en