Adaptive Trajectory Planning and Optimization at Limits of Handling

As deployment of automated vehicles increases, so does the rate at which they are exposed to critical traffic situations. Such situations, e.g. a late detected pedestrian in the vehicle path, require operation at the handling limits in order to maximize the capacity to avoid an accident. Also, the physical limitations of the vehicle typically vary in time due to local road and weather conditions. In this paper, we tackle the problem of trajectory planning and control at the limits of handling under time varying constraints, by adapting to local traction limitations. The proposed method is based on Real Time Iteration Sequential Quadratic Programming (RTI-SQP) augmented with state space sampling, which we call Sampling Augmented Adaptive RTI-SQP (SAA-SQP). Through extensive numerical simulations we demonstrate that our method increases the vehicle's capacity to avoid late detected obstacles compared to the traditional planning/tracking approaches, as a direct consequence of safe operating constraint adaptation in real time.