Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles

• URL: http://arxiv.org/abs/2309.13485v1
• Date: Sat, 23 Sep 2023 22:13:03 GMT
• Title: Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles
• Authors: Haolan Liu, Jishen Zhao, Liangjun Zhang
• Abstract summary: Prior work only learns to estimate a single planning trajectory, while there may be multiple acceptable plans in real-world scenarios. We propose an interpretable neural planner to regress a heatmap, which effectively represents multiple potential goals in the bird's-eye view of an autonomous vehicle. Our systematic evaluation on the Lyft Open dataset shows that our model achieves a safer and more flexible driving performance than prior works.
• Score: 22.396215670672852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based approaches to autonomous vehicle planners have the potential to scale to many complicated real-world driving scenarios by leveraging huge amounts of driver demonstrations. However, prior work only learns to estimate a single planning trajectory, while there may be multiple acceptable plans in real-world scenarios. To solve the problem, we propose an interpretable neural planner to regress a heatmap, which effectively represents multiple potential goals in the bird's-eye view of an autonomous vehicle. The planner employs an adaptive Gaussian kernel and relaxed hourglass loss to better capture the uncertainty of planning problems. We also use a negative Gaussian kernel to add supervision to the heatmap regression, enabling the model to learn collision avoidance effectively. Our systematic evaluation on the Lyft Open Dataset across a diverse range of real-world driving scenarios shows that our model achieves a safer and more flexible driving performance than prior works.

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