End-to-end autonomous driving aims to build a fully differentiable system that takes raw
sensor data as inputs and directly outputs the planned trajectory or control signals of the ego …

End-to-end autonomous driving has made impressive progress in recent years. Existing
methods usually adopt the decoupled encoder-decoder paradigm, where the encoder …

Autonomous vehicles operating in complex real-world environments require accurate
predictions of interactive behaviors between traffic participants. This paper tackles the …

End-to-end approaches to autonomous driving commonly rely on expert demonstrations.
Although humans are good drivers, they are not good coaches for end-to-end algorithms …

End-to-end autonomous driving recently emerged as a promising research direction to
target autonomy from a full-stack perspective. Along this line many of the latest works follow …

Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …

We present a learning-based planner that aims to robustly drive a vehicle by mimicking
human drivers' driving behavior. We leverage a mid-to-mid approach that allows us to …

Humans learn to drive through both practice and theory, eg by studying the rules, while most
self-driving systems are limited to the former. Being able to incorporate human knowledge of …

Current end-to-end autonomous driving methods either run a controller based on a planned
trajectory or perform control prediction directly, which have spanned two separately studied …

Autonomous driving systems need to handle complex scenarios such as lane following,
avoiding collisions, taking turns, and responding to traffic signals. In recent years …