Planning-oriented autonomous driving
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 …
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
Motionlm: Multi-agent motion forecasting as language modeling
Reliable forecasting of the future behavior of road agents is a critical component to safe
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
Scene transformer: A unified architecture for predicting multiple agent trajectories
Predicting the motion of multiple agents is necessary for planning in dynamic environments.
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …
Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving
Autonomous vehicles operating in complex real-world environments require accurate
predictions of interactive behaviors between traffic participants. This paper tackles the …
predictions of interactive behaviors between traffic participants. This paper tackles the …
Rethinking integration of prediction and planning in deep learning-based automated driving systems: a review
Automated driving has the potential to revolutionize personal, public, and freight mobility.
Besides the enormous challenge of perception, ie accurately perceiving the environment …
Besides the enormous challenge of perception, ie accurately perceiving the environment …
Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving
Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …
Deep interactive motion prediction and planning: Playing games with motion prediction models
Abstract In most classical Autonomous Vehicle (AV) stacks, the prediction and planning
layers are separated, limiting the planner to react to predictions that are not informed by the …
layers are separated, limiting the planner to react to predictions that are not informed by the …
MoST: Multi-modality Scene Tokenization for Motion Prediction
Many existing motion prediction approaches rely on symbolic perception outputs to generate
agent trajectories such as bounding boxes road graph information and traffic lights. This …
agent trajectories such as bounding boxes road graph information and traffic lights. This …
Uncertainty-aware model-based offline reinforcement learning for automated driving
Offline reinforcement learning (RL) provides a framework for learning decision-making from
offline data and therefore constitutes a promising approach for real-world applications such …
offline data and therefore constitutes a promising approach for real-world applications such …
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 …