A Research Testbed for Intelligent and Cooperative Driving in Mixed Traffic

Autonomous vehicles are gradually entering the transportation system. The traffic will become more heterogeneous since both autonomous and human-driven vehicles will share the roads. Cooperative driving, by promoting synchronized actions and shared situational awareness among vehicles, can significantly enhance driving safety. On the other hand, understanding human drivers is a pivotal step for cooperative driving in such mixed traffic environments, which facilitates effective interaction between human drivers and their vehicles. This paper presents a testbed that can be used to conduct research in intelligent and cooperative driving. The testbed consists of driving simulators, custom-designed copilots with an Artificial Intelligence engine, an optimization server, and a cloud database. The copilot is capable of sensing and understanding the human driver, the vehicle and the traffic. It can assist the driver by providing timely alerts on potential risks. Most importantly, it can communicate with other nearby vehicles for cooperative driving. Two case studies are presented to validate and evaluate the testbed. The first case study demonstrates the performance of the copilot in human distraction detection and driving assistance. The second case study focuses on cooperative driving between one human-driven vehicle and two connected autonomous vehicles in a lane-changing scenario. We expect this research testbed to be used in various research projects that involve human-driven vehicles and connected autonomous vehicles.