A margin-based approach to vehicle threat assessment in a homotopy framework for semi-autonomous highway navigation

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 139-145).This thesis describes the design of an unified framework for threat assessment and the shared control between a human driver and the onboard controller, based on the notion of fields of safe travel. It allows to perform corridor navigation, trajectory planning, threat assessment and driving assistance in hazardous situations. A new approach to the threat assessment problem is introduced, based upon the estimation of the control freedom afforded to a vehicle. Given sensor information of the surrounding environment, an algorithm first identifies corridors of travel through which the vehicle can safely navigate. The second stage then consists in assessing the potential threat posed to the vehicle in each identified corridor thanks to a metric associated with available control margin. For this purpose, the fields of safe travel are associated with sets of homotopic trajectories generated either from a lattice sampled in the vehicles input space or from a conformal state lattice. This level of threat is the keystone of the system and serves as input to influence autonomous navigation or driver support inputs. The semi-autonomous control system aims to honor safe driver inputs while ensuring safe and robust navigation properties. It ideally operates only during instances of significant threat: it should give a driver full control of the vehicle in "low threat" situations but apply appropriate levels of computer-controlled actuator effort during "high threat" situations. This approach preserves the freedom of control of the human driver when he/she remains within a safe navigable corridor, and adjust the vehicle trajectory when its predicted future state falls out of a safe field, or when the lowest threat exceeds some threshold. In fully autonomous mode, this human-inspired motion planning approach ensures collision free navigation and driving comfort.by Alexandre Constantin.S.M