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A Learning-Based Model Predictive Trajectory Planning Controller for Automated Driving in Unstructured Dynamic Environments
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AI
DOI：10.1109/TVT.2022.3159994.png)
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Abstract
En 中文
This paper presents a learning-based model predictive trajectory planning controller for automated driving in unstructured, dynamic environments with obstacle avoidance. We first address the problem of lacking prior knowledge in unstructured environments by introducing a risk map that maps the density and motion of obstacles and the road to an occupancy risk. Model predictive control is then used to integrate trajectory planning and tracking control into one framework to bridge the gap between planning and control. Meanwhile, we use Gaussian Process (GP) regression to learn the residual model uncertainty for improving the model accuracy. An objective function considering both risks within the feasible region and vehicle dynamics is carefully formulated to obtain collision-free and kinematically-feasible local trajectories. Field experiments are performed on real unstructured environments with our automated vehicle. Experimental results demonstrate the effectiveness of the proposed algorithm for successful obstacle avoidance in various complex unstructured scenarios.
Keywords:
Vehicle dynamics
Roads
Trajectory planning
Planning
Uncertainty
Trajectory
Costs
Trajectory planning
automated vehicles
gauss-ian processes
model predictive control
unstructured environ-ments
risk map
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