Decision Making and Control for Automotive Safety
This thesis proposes a novel automotive safety function that utilizes information about the host vehicle's state and the road ahead to predict and prevent unintended roadway departures. For this purpose predictive threat assessment, decision making and control algorithms are developed. The developed algorithms take into account fundamental limitations in a vehicle's dynamical capabilities while using road information to maintain the vehicle's maneuverability and keep it on the road.
Particular attention is given to the threat assessment problem. A threat assessment algorithm that activates interventions when it can be theoretically guaranteed that it is no longer possible for a driver to avoid departing the road or losing vehicle maneuverability is developed. The algorithm is based on reachability analysis tools for linear systems. An algorithm that recursively estimates the driver's steering behavior as an affine function of the vehicle state is also developed. The explicit representation of the driver's steering behavior is used to form an alternative threat assessment algorithm that, in addition to considering vehicle dynamics, accounts for limitations in the driver's capabilities. Moreover, it is shown how uncertainty in the state, disturbance and parameter estimates can be accounted for in order to maintain the theoretical guarantees of avoiding unnecessary intervention activation also in the presence of uncertainty. In order to maintain such guarantees considering model parameter uncertainty, we derive and prove theoretical results. In addition, a threat assessment algorithm that accounts for the nonlinearities in the system dynamics that are exhibited e.g. during combined braking and steering is developed. For this purpose, we use interval based consistency techniques to solve the threat assessment problem.
The developed methods are validated using simulations, logged experimental data and real-time experiments.