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Hierarchical Planning and Control for Self-Driving Cars

Published on:

7 February 2024

Primary Category:

Robotics

Paper Authors:

Hung Duy Nguyen,

Minh Nhat Vu,

Nguyen Ngoc Nam,

Kyoungseok Han

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Key Details

Proposes two-layered planning and control strategy for self-driving cars

Upper layer generates optimal trajectories avoiding obstacles

Lower layer tracks trajectories using robust predictive control

Achieves good balance of tracking, stability and efficiency

Outperforms other robust predictive control methods

AI generated summary

Hierarchical Planning and Control for Self-Driving Cars

This paper proposes a two-layered approach for self-driving cars to handle complex driving scenarios and uncertain vehicle parameters. The upper layer uses artificial potential fields to generate optimal trajectories avoiding obstacles. The lower layer employs a robust model predictive controller to track these trajectories robustly. Compared to other methods, this approach balances tracking performance, stability, and computational efficiency.

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