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Learning-Based Stochastic Model Predictive Control for Autonomous Driving at Uncontrolled Intersections
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DOI：10.1109/TITS.2024.3510041.png)
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Abstract
En 中文
Autonomous driving in urban environments requires safe control policies that account for the non-determinism of moving obstacles, such as the position other vehicles will take while crossing an uncontrolled intersection. We address this problem by proposing a stochastic model predictive control (MPC) approach with robust collision avoidance constraints to guarantee safety. By adopting a stochastic formulation, the quality of closed-loop tracking is increased by avoiding giving excessive importance to future obstacle configurations that are unlikely to occur. We compute the probabilities associated with different obstacle trajectories by learning a classifier on a realistic dataset generated by the microscopic traffic simulator SUMO and show the benefits of the proposed stochastic MPC formulation on a simulated realistic intersection.
Keywords:
Autonomous vehicles
model predictive control
model predictive control
scenario trees
scenario trees
stochastic model predictive control
stochastic model predictive control
supervised learning
supervised learning
classification methods
classification methods
decision trees
decision trees
Journal
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8.4
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9.5K
Citations:
6.3W
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