Collision Avoidance Guardian at the Dynamic Limits of Handling
Project Abstract/Statement of Work:
This project aims to solve the problem of collision avoidance when collision can be avoided only if the vehicle is maneuvered at its dynamic limits of handling. In this context, dynamic limits refer to events that can cause loss of control over the vehicle, such as excessive sideslip or tire lift-off. Maneuvering a vehicle at its dynamic limits becomes necessary if the driver is unable to react to a situation fast enough (e.g., due to limitations of human response times, or distraction) and the safe braking distance is too long to avoid collision with an obstacle by braking alone (e.g., due to high speed, or low friction). In these situations, an autonomous collision avoidance algorithm with the ability of maneuvering the vehicle at its dynamics limits can serve as a guardian to avoid collision. The goal of this one-year pilot project is to develop this guardian feature and perform pilot studies with driver-in-the-loop simulations to evaluate its effectiveness.
Current obstacle avoidance approaches can be classified into four categories: graph search based methods, virtual potential and navigation function based methods, meta-heuristic methods, and mathematical optimization based methods. The first three categories cannot guarantee obstacle avoidance at the dynamic limits, as they do not account for the dynamic limits of the vehicle explicitly. Mathematical optimization provides the formalism needed to explicitly include dynamic limits; however, the existing collision avoidance formulations do not consider all the constraints needed to be able to maneuver a vehicle at its dynamic limits.
To solve the above mentioned problem, we will use a model predictive control framework. In this framework, a low-order model of the vehicle will be used to predict the vehicle trajectory for a short time horizon into the future for given steering, throttle, and brake commands. A mathematical optimization will be set up with this vehicle model as part of the dynamic constraints. Additional constraints will include actuator and safety constraints (e.g., no tire lift-off). The goal of this optimization will be to find the optimal steering, throttle and brake commands to avoid collision with a maximum safety margin. This optimization will be repeated every few hundred milliseconds with a prediction horizon of a few seconds to refresh the optimal control commands according to the new measurements from the vehicle and environment (e.g., vehicle position, sensor data, etc.). The resulting steering, throttle and brake commands will not be applied to the vehicle unless a specified minimum safety margin is reached. At that point the algorithm will take over the control of the vehicle to avoid the collision and then give control back to the driver once the vehicle is safe again. A driver-in-the-loop simulator with a high-order vehicle model will be used for evaluation purposes.