A Cooperative Crash Avoidance Framework for Autonomous Vehicle under Collision-Imminent Situations in Mixed Traffic Stream
Runjia Du, Sikai Chen, Yujie Li, Paul Ha, Jiqian Dong, Panagiotis Ch. Anastasopoulos, Samuel Labi
Abstract
Autonomous vehicles (AVs) are expected to increase the safety of transportation systems because automation minimizes human error in driving tasks. It is likely that such benefits will be fully manifested only when AV market penetration reaches 100%. However, the transition from a system of human-driven vehicles (HDVs) dominant to AVs dominant is expected to be time consuming. Thus, the safety benefits of AVs will be curtailed by the human error persisting through the human-driven vehicles (HDVs) during mixed traffic flow comprised of both AVs and HDVs. Such heterogeneity causes unsafe traffic operations maneuvers due particularly to the errant nature of human driving, especially in high-velocity lane-change maneuver. In this study, two perspectives of human error under the mixed traffic environment are proposed: 1) human error from inside of the vehicles; 2) human error from outside. This paper focuses on the second perspective, in the context of aggressive lane-change HDV. By formulating a Model Predictive Control (MPC) and V2V based cooperative framework, the AVs in such situations will be able to avoid side-impact and rear-end collision with the aggressive HDV. The framework is tested under different traffic conditions in terms of the vehicle bumper-to-bumper distance and relative velocities. The crash avoidance success rate averages at 90%, even reaches 100% when the relative velocity was low.