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.

翻译：基于学习的自动驾驶车辆规划方法通过利用大量驾驶员演示，有望扩展到许多复杂的现实驾驶场景。然而，先前的工作仅学习估计单一规划轨迹，而现实场景中可能存在多个可接受的规划方案。为了解决这一问题，我们提出了一种可解释的神经规划器，用于回归热力图，该热力图有效表示了自动驾驶车辆鸟瞰图中的多个潜在目标。该规划器采用自适应高斯核和松弛的沙漏损失，以更好地捕捉规划问题的不确定性。我们还使用负高斯核为热力图回归添加监督，使模型能够有效学习避免碰撞。我们在Lyft开放数据集上对多种现实驾驶场景进行了系统评估，结果表明，与先前工作相比，我们的模型实现了更安全且更灵活的驾驶性能。