AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
As an essential task in autonomous driving (AD), motion prediction aims to predict the future
states of surround objects for navigation. One natural solution is to estimate the position of …
states of surround objects for navigation. One natural solution is to estimate the position of …
Dream to Drive With Predictive Individual World Model
Y Gao, Q Zhang, DW Ding… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
It is still a challenging topic to make reactive driving behaviors in complex urban
environments as road users' intentions are unknown. Model-based reinforcement learning …
environments as road users' intentions are unknown. Model-based reinforcement learning …
Solving Motion Planning Tasks with a Scalable Generative Model
As autonomous driving systems being deployed to millions of vehicles, there is a pressing
need of improving the system's scalability, safety and reducing the engineering cost. A …
need of improving the system's scalability, safety and reducing the engineering cost. A …
MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly
We present MBAPPE, a novel approach to motion planning for autonomous driving
combining tree search with a partially-learned model of the environment. Leveraging the …
combining tree search with a partially-learned model of the environment. Leveraging the …
iMotion-LLM: Motion Prediction Instruction Tuning
A Felemban, EM Bakr, X Shen, J Ding… - arXiv preprint arXiv …, 2024 - arxiv.org
We introduce iMotion-LLM: a Multimodal Large Language Models (LLMs) with trajectory
prediction, tailored to guide interactive multi-agent scenarios. Different from conventional …
prediction, tailored to guide interactive multi-agent scenarios. Different from conventional …
Manipulating Trajectory Prediction with Backdoors
Autonomous vehicles ought to predict the surrounding agents' trajectories to allow safe
maneuvers in uncertain and complex traffic situations. As companies increasingly apply …
maneuvers in uncertain and complex traffic situations. As companies increasingly apply …
NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction
J Lidard, O So, Y Zhang, J DeCastro, X Cui… - arXiv preprint arXiv …, 2023 - arxiv.org
Interactions between road agents present a significant challenge in trajectory prediction,
especially in cases involving multiple agents. Because existing diversity-aware predictors do …
especially in cases involving multiple agents. Because existing diversity-aware predictors do …
Think Deep and Fast: Learning Neural Nonlinear Opinion Dynamics from Inverse Dynamic Games for Split-Second Interactions
Non-cooperative interactions commonly occur in multi-agent scenarios such as car racing,
where an ego vehicle can choose to overtake the rival, or stay behind it until a safe …
where an ego vehicle can choose to overtake the rival, or stay behind it until a safe …
Asynchronous Large Language Model Enhanced Planner for Autonomous Driving
Despite real-time planners exhibiting remarkable performance in autonomous driving, the
growing exploration of Large Language Models (LLMs) has opened avenues for enhancing …
growing exploration of Large Language Models (LLMs) has opened avenues for enhancing …
PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving
We present PLUTO, a powerful framework that pushes the limit of imitation learning-based
planning for autonomous driving. Our improvements stem from three pivotal aspects: a …
planning for autonomous driving. Our improvements stem from three pivotal aspects: a …