With the recent advancement of deep learning technology, data-driven
approaches for autonomous car prediction and planning have achieved
extraordinary performance. Nevertheless, most of these approaches follow a
non-interactive prediction and planning paradigm, hypothesizing that a
vehicle's behaviors do not affect others. The approaches based on such a
non-interactive philosophy typically perform acceptably in sparse traffic
scenarios but can easily fail in dense traffic scenarios. Therefore, we propose
an end-to-end interactive neural motion planner (INMP) for autonomous driving
in this paper. Given a set of past surrounding-view images and a high
definition map, our INMP first generates a feature map in bird's-eye-view
space, which is then processed to detect other agents and perform interactive
prediction and planning jointly. Also, we adopt an optical flow distillation
paradigm, which can effectively improve the network performance while still
maintaining its real-time inference speed. Extensive experiments on the
nuScenes dataset and in the closed-loop Carla simulation environment
demonstrate the effectiveness and efficiency of our INMP for the detection,
prediction, and planning tasks. Our project page is at
sites.google.com/view/inmp-ofd.

本文提出了一种基于深度学习技术的交互式神经运动规划器 (INMP)，通过过去的周边图像和高清地图，实时检测其他车辆并进行交互式的预测和规划，采用光流蒸馏模型来提高网络性能，实现自动驾驶任务的有效性和高效性。

基于光流蒸馏的自动驾驶端到端交互式预测和规划

End-to-End Interactive Prediction and Planning with Optical Flow  Distillation for Autonomous Driving

High-definition maps (HD maps) are a key component of most modern
self-driving systems due to their valuable semantic and geometric information.
Unfortunately, building HD maps has proven hard to scale due to their cost as
well as the requirements they impose in the localization system that has to
work everywhere with centimeter-level accuracy. Being able to drive without an
HD map would be very beneficial to scale self-driving solutions as well as to
increase the failure tolerance of existing ones (e.g., if localization fails or
the map is not up-to-date). Towards this goal, we propose MP3, an end-to-end
approach to mapless driving where the input is raw sensor data and a high-level
command (e.g., turn left at the intersection). MP3 predicts intermediate
representations in the form of an online map and the current and future state
of dynamic agents, and exploits them in a novel neural motion planner to make
interpretable decisions taking into account uncertainty. We show that our
approach is significantly safer, more comfortable, and can follow commands
better than the baselines in challenging long-term closed-loop simulations, as
well as when compared to an expert driver in a large-scale real-world dataset.

提出了一种无地图自主驾驶的端到端方法，该方法以原始传感器数据和高级命令（例如，在十字路口左转）作为输入，利用在线地图以及动态代理的当前和未来状态性能预测，并利用一种新颖的神经运动规划器制定可解释决策，同时能够更好地执行命令和比基线和专家驾驶员更加安全和舒适地执行长期封闭环模拟。

MP3：一个统一的模型用于映射、感知、预测和规划

MP3: A Unified Model to Map, Perceive, Predict and Plan

In this paper, we propose a neural motion planner (NMP) for learning to drive
autonomously in complex urban scenarios that include traffic-light handling,
yielding, and interactions with multiple road-users. Towards this goal, we
design a holistic model that takes as input raw LIDAR data and a HD map and
produces interpretable intermediate representations in the form of 3D
detections and their future trajectories, as well as a cost volume defining the
goodness of each position that the self-driving car can take within the
planning horizon. We then sample a set of diverse physically possible
trajectories and choose the one with the minimum learned cost. Importantly, our
cost volume is able to naturally capture multi-modality. We demonstrate the
effectiveness of our approach in real-world driving data captured in several
cities in North America. Our experiments show that the learned cost volume can
generate safer planning than all the baselines.

本文提出了一种神经运动规划器 (NMP)，用于在复杂的城市场景中学习自主驾驶，包括交通灯处理、让路和与多个道路用户的交互。通过输入原始的 LIDAR 数据和高清地图，NMP 生成可解释的中间表示形式，以及定义了自动驾驶汽车在规划范围内可以采取的每个位置的良好程度的成本体积。最后，本文的实验结果表明，学习成本体积可以生成比所有基线更安全的规划。