TR2020-096
Integrated Obstacle Detection and Avoidance in Motion Planning and Predictive Control of Autonomous Vehicles
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- "Integrated Obstacle Detection and Avoidance in Motion Planning and Predictive Control of Autonomous Vehicles", American Control Conference (ACC), DOI: 10.23919/ACC45564.2020.9147820, July 2020.BibTeX TR2020-096 PDF
- @inproceedings{Quirynen2020jul,
- author = {Quirynen, Rien and Berntorp, Karl and Kambam, Karthik and Di Cairano, Stefano},
- title = {Integrated Obstacle Detection and Avoidance in Motion Planning and Predictive Control of Autonomous Vehicles},
- booktitle = {American Control Conference (ACC)},
- year = 2020,
- month = jul,
- doi = {10.23919/ACC45564.2020.9147820},
- url = {https://www.merl.com/publications/TR2020-096}
- }
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- "Integrated Obstacle Detection and Avoidance in Motion Planning and Predictive Control of Autonomous Vehicles", American Control Conference (ACC), DOI: 10.23919/ACC45564.2020.9147820, July 2020.
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Research Areas:
Abstract:
This paper presents a novel approach for obstacle avoidance in autonomous driving systems, based on a hierarchical software architecture that involves both a lowrate, long-term motion planning algorithm and a high-rate, highly reactive predictive controller. More specifically, an integrated framework of a particle-filter based motion planner is proposed in combination with a trajectory-tracking algorithm using nonlinear model predictive control (NMPC). The motion planner computes a reference trajectory to be tracked, and its corresponding covariance is used for automatically tuning the time-varying tracking cost in the NMPC problem formulation. Preliminary experimental results, based on a test platform of small-scale autonomous vehicles, illustrate that the propose approach can enable safe obstacle avoidance and reliable driving behavior in relatively complex scenarios.
Related News & Events
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NEWS MERL researchers presented 10 papers at American Control Conference (ACC) Date: July 1, 2020 - July 3, 2020
Where: Denver, Colorado (virtual)
MERL Contacts: Mouhacine Benosman; Ankush Chakrabarty; Stefano Di Cairano; Yebin Wang; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference, MERL presented 10 papers on subjects including autonomous-vehicle decision making and motion planning, nonlinear estimation for thermal-fluid models and GNSS positioning, learning-based reference governors and reference governors for railway vehicles, and fail-safe rendezvous control.