Autonomous vehicles rely on their perception systems to acquire information about their
immediate surroundings. It is necessary to detect the presence of other vehicles …

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving
experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The …

We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model
into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge …

An accurate model of the environment and the dynamic agents acting in it offers great
potential for improving motion planning. We present MILE: a Model-based Imitation …

In this paper, we present a system to train driving policies from experiences collected not just
from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of …

The release of nuPlan marks a new era in vehicle motion planning research, offering the first
large-scale real-world dataset and evaluation schemes requiring both precise short-term …

Human-level driving is an ultimate goal of autonomous driving. Conventional approaches
formulate autonomous driving as a perception-prediction-planning framework, yet their …

We present Argoverse, a dataset designed to support autonomous vehicle perception tasks
including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a …

Object detection in point clouds is an important aspect of many robotics applications such as
autonomous driving. In this paper, we consider the problem of encoding a point cloud into a …

Driving requires reacting to a wide variety of complex environment conditions and agent
behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation …