Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments. However, their complex nature and lack of transparency can hinder explainability and trustworthiness and complicate safety assurance. Motivated by these challenges, we propose a hybrid planning architecture that combines the advantages of machine learning with the verifiability and the determinism of classical approaches. Specifically, we developed a deep neural network to interpret complex traffic scenes and propose driving behavior, while an optimization-based supervision layer validate...
| Authors | Jean-Pierre Busch, Guido Linden, Jan Bergmann et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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