Vehicle crash mitigation strategy in unavoidable collision scenarios: focusing on motion planning by considering a generalized crash severity model

Abstract

Driving strategy in dynamic environment is crucial to the automated vehicle safety. In extremely emergency scenarios with unavoidable collisions, especially those with complex impact patterns, the potential crash risk should be well considered. This paper proposes a crash mitigation algorithm for unavoidable collisions, which directly embeds a generalized crash severity index model to vehicle-to-vehicle collisions of multiple impact patterns. The idea is that during the short time before a collision, the vehicle will actively adapt its position and poses to minimize the potential crash severity level after the collision. To this end, the generalized crash severity index (CSI) model is introduced to estimate the potential crash severity of all sample paths, from which a crash-severityoptimal trajectory is obtained. To improve the inferring time efficiency of the planning module, a neural network is constructed and deployed to approximate the nonlinear severity model. The proposed algorithm is first validated through simulations of unavoidable collision scenarios, Vehicle Crash Mitigation Considering CSI model including entry ramp merging, intersection crossing and downhill/uphill crossing. Then for the intersection crossing scenario, the algorithm is deployed to a real car and validated through digital-twin experiments. Results show that by combining the braking and steering interventions for better crash severity reduction, the proposed strategy can achieve better mitigation effects than commonly-used collision-avoidance strategies. This reveals that a new mindset of comprehensive safety strategy should not focus only on collision avoidance, but also the last resort of crash mitigation if collision is unavoidable. Our work may contribute as a promising solution to the safety problem in emergency scenarios.

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

“…Driving is a social activity that involves endless interactions with other agents on the road, so locating these agents and predicting their possible future actions in the environment is essential for the safety of all agents. A crash mitigation algorithm that directly incorporates a generalized crash severity index model for vehicle-to-vehicle collisions of multiple impact patterns was proposed by Li et al [74]. The algorithm tries to adapt the vehicle position before the collision to minimize the impact's severity.…”