Transfuser: Imitation with transformer-based sensor fusion for autonomous driving
How should we integrate representations from complementary sensors for autonomous
driving? Geometry-based fusion has shown promise for perception (eg, object detection …
driving? Geometry-based fusion has shown promise for perception (eg, object detection …
Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research
Simulation is an essential tool to develop and benchmark autonomous vehicle planning
software in a safe and cost-effective manner. However, realistic simulation requires accurate …
software in a safe and cost-effective manner. However, realistic simulation requires accurate …
Generating useful accident-prone driving scenarios via a learned traffic prior
Evaluating and improving planning for autonomous vehicles requires scalable generation of
long-tail traffic scenarios. To be useful, these scenarios must be realistic and challenging …
long-tail traffic scenarios. To be useful, these scenarios must be realistic and challenging …
Parting with misconceptions about learning-based vehicle motion planning
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 …
large-scale real-world dataset and evaluation schemes requiring both precise short-term …
Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driving scenarios
Imitation learning (IL) is a simple and powerful way to use high-quality human driving data,
which can be collected at scale, to produce human-like behavior. However, policies based …
which can be collected at scale, to produce human-like behavior. However, policies based …
Safe real-world autonomous driving by learning to predict and plan with a mixture of experts
The goal of autonomous vehicles is to navigate public roads safely and comfortably. To
enforce safety, traditional planning approaches rely on handcrafted rules to generate …
enforce safety, traditional planning approaches rely on handcrafted rules to generate …
Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning
In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …
vehicles plan future trajectories. We develop a partially observable Markov decision process …
Occworld: Learning a 3d occupancy world model for autonomous driving
Understanding how the 3D scene evolves is vital for making decisions in autonomous
driving. Most existing methods achieve this by predicting the movements of object boxes …
driving. Most existing methods achieve this by predicting the movements of object boxes …
Instinctive Negotiation by Autonomous Agents in Dense, Unstructured Traffic: A Controls Perspective
M Jankovic - Annual Review of Control, Robotics, and …, 2024 - annualreviews.org
Operating autonomous agents in unstructured space presents a difficult problem. The
complexity of making decisions such as when to yield and when to go ahead increases …
complexity of making decisions such as when to yield and when to go ahead increases …
InterSim: Interactive traffic simulation via explicit relation modeling
Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for
planners in a more scalable and safe way compared to real-world road testing. Existing …
planners in a more scalable and safe way compared to real-world road testing. Existing …