Intelligent Avoidance Maneuvers for Autonomous Ground Vehicles

The project focuses on the development of new formulations of objective functions and constraints for motion-planning problems solved by trajectory optimization.

Vehicular systemsA time-critical avoidance maneuver. Photo credit iStock/Toa55This research project addresses planning of time-critical avoidance maneuvers, with applications to autonomous ground vehicles. The project focuses on the development of new formulations of objective functions and constraints for motion-planning problems solved by trajectory optimization. The overall objective is to increase the safety for vehicles by utilizing the new sensor capabilities and situation awareness as well as the extended level of actuation possibilities available or foreseen in ground vehicles. One of the major challenges in application of optimization techniques in vehicle control is to satisfy the computational constraints, in light of the inherent time-critical nature of the maneuver to be performed.

The project is performed along two different complementary paths. The first part of the research considers new formulations of optimization problems for planning a safe motion of a vehicle in different critical scenarios such as where a double lane-change maneuver is required. The second part of the research investigates how the computational complexity of the optimization problem for determining the optimal maneuvers can be reduced. More specifically, methods for decomposition and parallel computation of segments of the full maneuver have been developed. Such methods decrease the total computational time, and are thus of interest towards the goal of being able to online compute a full vehicle maneuver using optimization.

Experimental evaluations are planned within the framework of the WASP Autonomous Research Arena (WARA) for automated transport systems, and specifically with the planned experimental car.


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2019

2018

WASP research vehicular systems
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