Post Impact Vehicle Path Control in Multiple Event Accidents

Licentiatavhandling, 2011

Recent statistics show that multiple-event accidents (MEAs) possess an increasing fraction of all vehicle traffic accidents. They are characterized by having at least one vehicle subjected to more than one harmful event, such as collision with another vehicle. MEAs now comprise approximately 25% of all passenger vehicle accidents. The scope of the research presented in this thesis is to develop vehicle Post Impact Control (PIC) strategies in order to avoid or mitigate any secondary events in MEAs. To characterize the problematic areas for PIC, an analysis was conducted in an in-depth accident database: cars which have potentials to gain safety benefits from PIC were identified; several representative accident cases were selected; post impact vehicle dynamics were analyzed taking the overall accident scenario into account; benefit measures were determined for each case. It was found that reduction of kinetic energy and path lateral deviation would be mostly beneficial under post impact circumstances. To analyze the capability of influencing vehicle path after the first impact, trajectory optimization was done using braking sequences which minimize the maximum path lateral deviation. It was found that effective control across the very wide range of post impact kinematic conditions can be significantly achieved by switching between three sub-strategies established on vehicle body level. The resulting interventions were found to be qualitatively different to classical Electronic Stability Control (ESC) systems, and also to interventions proposed in other studies on post-impact control which prioritize the minimization of any large post impact yaw rate or longitudinal velocity. For closed-loop implementation of the path controller, one of the three sub-strategies is particularly challenging: control of global lateral force and yaw moment simultaneously. A closed-loop quasi-linear optimal controller (QLOC) was proposed and verified against the trajectory optimization results.