TR2020-096

Integrated Obstacle Detection and Avoidance in Motion Planning and Predictive Control of Autonomous Vehicles


    •  Quirynen, R., Berntorp, K., Kambam, K., Di Cairano, S., "Integrated Obstacle Detection and Avoidance in Motion Planning and Predictive Control of Autonomous Vehicles", American Control Conference (ACC), 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,
      • url = {https://www.merl.com/publications/TR2020-096}
      • }
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  • Research Areas:

    Control, Optimization

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.

 

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