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

TL;DR

MP3 introduces an end-to-end, mapless driving approach that predicts dynamic and static environment representations from raw sensor data, enabling safer and more reliable autonomous driving without relying on high-definition maps.

Contribution

The paper presents MP3, a novel neural model that integrates perception, prediction, and planning for mapless autonomous driving using raw sensor inputs.

Findings

Outperforms baselines in safety and comfort in simulations

Achieves better command following than existing methods

Demonstrates effectiveness in real-world driving scenarios

Abstract

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.