Title
Long Horizon Risk-Averse Motion Planning: A Model-Predictive Approach
Author
van der Ploeg, C.
Smit, R.
Teerhuis, A.
Silvas, E.
Publication year
2022
Abstract
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.
Subject
Cost functions
Intelligent vehicle highway systems
Motion planning
Predictive control systems
Risk analysis
Roads and streets
Safety engineering
Trajectories
Vehicles
Automated vehicles
Mixed traffic
Model predictive
Model-predictive control
Motion-planning
Non-linear modelling
Planning method
Risk averse
Road users
Trajectory generator
Model predictive control
To reference this document use:
http://resolver.tudelft.nl/uuid:6f79ee80-47c5-4493-a2f8-2d9c6344ceb8
DOI
https://doi.org/10.1109/itsc55140.2022.9921750
TNO identifier
979647
Publisher
Institute of Electrical and Electronics Engineers Inc.
ISBN
9781665468800
Source
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, 1141-1148
Document type
conference paper