Decision-theoretic MPC: Motion Planning with Weighted Maneuver Preferences Under Uncertainty

TL;DR

This paper presents a decision-theoretic model for motion planning that combines multiple maneuver options with weighted preferences under uncertainty, improving safety and interaction in autonomous driving.

Contribution

It introduces a continuous optimization motion planner that integrates multiple maneuvers with weighted preferences, handling uncertainties and avoiding commitment to a single maneuver.

Findings

Enhanced interaction capabilities in driving scenarios

Improved comfort levels over traditional planners

Maintains safety with chance-constrained emergency maneuvers

Abstract

Continuous optimization based motion planners require specifying a maneuver class before calculating the optimal trajectory for that class. In traffic, the intentions of other participants are often unclear, presenting multiple maneuver options for the autonomous vehicle. This uncertainty can make it difficult for the vehicle to decide on the best option. This work introduces a continuous optimization based motion planner that combines multiple maneuvers by weighting the trajectory of each maneuver according to the vehicle's preferences. In this way, the planner eliminates the need for committing to a single maneuver. To maintain safety despite this increased complexity, the planner considers uncertainties ranging from perception to prediction, while ensuring the feasibility of a chance-constrained emergency maneuver. Evaluations in both driving experiments and simulation studies show enhanced interaction capabilities and comfort levels compared to conventional planners, which consider only a single maneuver.