Autonomous Vehicle Trajectory Planning for Minimum Position Uncertainty

Adam Rutkowski, Yetong Zhang, and Frank Dellaert

Peer Reviewed

Abstract:	Position estimation accuracy often depends on the path that is traveled, particularly in the absence of measurements to globally referenced sources such as GPS. For autonomous vehicles operating in an open world environment, the vehicle trajectories may be planned to minimize the expected position estimation uncertainty. Our approach uses automatic differentiation of a scalar valued function of the expected final covariance with respect to control point locations that define the path with B-splines. A gradient descent or quasi-Newton method is then used to determine path parameters that optimize the objective. For a collaborative navigation scenario, this method is a significant improvement compared to a derivative-free approach. Index Terms—state estimation, cooperative navigation, constrained optimization, trajectory optimization
Published in:	2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 28 - 1, 2025 Salt Lake Marriott Downtown at City Creek Salt Lake City, UT
Pages:	1417 - 1423
Cite this article:	Rutkowski, Adam, Zhang, Yetong, Dellaert, Frank, "Autonomous Vehicle Trajectory Planning for Minimum Position Uncertainty," 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), Salt Lake City, UT, April 2025, pp. 1417-1423.
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