Longitudinal Safety Analysis For Heterogeneous Platoon Of Automated And Human Vehicles
For researchers and engineers working on mixed traffic safety, this study provides comparative analysis of collision avoidance algorithms, though findings are incremental and simulation-based.
This paper investigates the impact of four collision avoidance algorithms on crash rate and severity in a heterogeneous platoon of automated and human-driven vehicles at different market penetration rates. Results show varying safety outcomes depending on algorithm and penetration rate.
With the recent advancement in environmental sensing, vehicle control and vehicle-infrastructure cooperation technologies, more and more autonomous driving companies start to put their intelligent cars into road test. But in the near future, we will face a heterogeneous traffic with both intelligent connected vehicles and human vehicles. In this paper, we investigated the impacts of four collision avoidance algorithms under different intelligent connected vehicles market penetration rate. A customized simulation platform is built, in which a platoon can be initiated with many key parameters. For every short time interval, the dynamics of vehicles are updated and input in a kinematics model. If a collision occurs, the energy loss is calculated to represent the crash severity. Four collision avoidance algorithms are chosen and compared in terms of the crash rate and severity at different market penetration rate and different locations of the platoon. The results generate interesting debates on the issues of heterogeneous platoon safety.