Social motion prediction with cognitive hierarchies
Humans exhibit a remarkable capacity for anticipating the actions of others and planning
their own actions accordingly. In this study, we strive to replicate this ability by addressing …
their own actions accordingly. In this study, we strive to replicate this ability by addressing …
Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving
N Karnchanachari, D Geromichalos, KS Tan… - arXiv preprint arXiv …, 2024 - arxiv.org
Machine Learning (ML) has replaced traditional handcrafted methods for perception and
prediction in autonomous vehicles. Yet for the equally important planning task, the adoption …
prediction in autonomous vehicles. Yet for the equally important planning task, the adoption …
CaDeT: a Causal Disentanglement Approach for Robust Trajectory Prediction in Autonomous Driving
M Pourkeshavarz, J Zhang… - Proceedings of the IEEE …, 2024 - openaccess.thecvf.com
For safe motion planning in real-world autonomous vehicles require behavior prediction
models that are reliable and robust to distribution shifts. The recent studies suggest that the …
models that are reliable and robust to distribution shifts. The recent studies suggest that the …
SMART: Scalable Multi-agent Real-time Simulation via Next-token Prediction
W Wu, X Feng, Z Gao, Y Kan - arXiv preprint arXiv:2405.15677, 2024 - arxiv.org
Data-driven autonomous driving motion generation tasks are frequently impacted by the
limitations of dataset size and the domain gap between datasets, which precludes their …
limitations of dataset size and the domain gap between datasets, which precludes their …
Blending data-driven priors in dynamic games
As intelligent robots like autonomous vehicles become increasingly deployed in the
presence of people, the extent to which these systems should leverage model-based game …
presence of people, the extent to which these systems should leverage model-based game …
Versatile Scene-Consistent Traffic Scenario Generation as Optimization with Diffusion
Generating realistic and controllable agent behaviors in traffic simulation is crucial for the
development of autonomous vehicles. This problem is often formulated as imitation learning …
development of autonomous vehicles. This problem is often formulated as imitation learning …
Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
Real-world autonomous driving systems must make safe decisions in the face of rare and
diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world …
diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world …
Pioneering se (2)-equivariant trajectory planning for automated driving
S Hagedorn, M Milich, AP Condurache - arXiv preprint arXiv:2403.11304, 2024 - arxiv.org
Planning the trajectory of the controlled ego vehicle is a key challenge in automated driving.
As for human drivers, predicting the motions of surrounding vehicles is important to plan the …
As for human drivers, predicting the motions of surrounding vehicles is important to plan the …
Instruct Large Language Models to Drive like Humans
Motion planning in complex scenarios is the core challenge in autonomous driving.
Conventional methods apply predefined rules or learn from driving data to plan the future …
Conventional methods apply predefined rules or learn from driving data to plan the future …
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 …