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 …
Prediction failure risk-aware decision-making for autonomous vehicles on signalized intersections
Motion prediction modules are crucial for autonomous vehicles to forecast the future
behavior of surrounding road users. Failures in prediction modules can mislead a …
behavior of surrounding road users. Failures in prediction modules can mislead a …
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 …
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 …
Llm-assist: Enhancing closed-loop planning with language-based reasoning
Although planning is a crucial component of the autonomous driving stack, researchers
have yet to develop robust planning algorithms that are capable of safely handling the …
have yet to develop robust planning algorithms that are capable of safely handling the …
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 …
Learning-aware safety for interactive autonomy
One of the outstanding challenges for the widespread deployment of robotic systems like
autonomous vehicles is ensuring safe interaction with humans without sacrificing efficiency …
autonomous vehicles is ensuring safe interaction with humans without sacrificing efficiency …
Occupancy prediction-guided neural planner for autonomous driving
Forecasting the scalable future states of surrounding traffic participants in complex traffic
scenarios is a critical capability for autonomous vehicles, as it enables safe and feasible …
scenarios is a critical capability for autonomous vehicles, as it enables safe and feasible …
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 …