Long Horizon Risk-Averse Motion Planning: A Model-Predictive Approach
conference paper
Developing safe automated vehicles that can be proactive, safe, and comfortable in mixed traffic requires improved planning methods that are risk-averse and that account for predictions of the motion of other road users. To consider these criteria, in this article, we propose a nonlinear model-predictive trajectory generator scheme, which couples the longitudinal and lateral motion of the vehicle to steer the vehicle with minimal risk, while progressing towards the goal state. The proposed method takes into account the infrastructure, surrounding objects, and predictions of the objects' state through artificial potential-based risk fields included in the cost function of the model-predictive control (MPC) problem. This trajectory generator enables anticipatory maneuvers, i.e., mitigating risk far before any safety-critical intervention would be necessary. The method is proven in several case studies representing both highways- and urban situations. The results show the safe and efficient implementation of the MPC trajectory generator while proving its real-time applicability.
Topics
Cost functionsIntelligent vehicle highway systemsMotion planningPredictive control systemsRisk analysisRoads and streetsSafety engineeringTrajectoriesVehiclesAutomated vehiclesMixed trafficModel predictiveModel-predictive controlMotion-planningNon-linear modellingPlanning methodRisk averseRoad usersTrajectory generatorModel predictive control
TNO Identifier
979647
ISBN
9781665468800
Publisher
Institute of Electrical and Electronics Engineers Inc.
Source title
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022, 8 October 2022 through 12 October 2022
Pages
1141-1148
Files
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