6 accepted papers
Ensuring safe and socially compliant driving is essential for autonomous vehicle planning. However, one of the significant challenges remains the performance bottleneck caused by interaction uncertainty in complex traffic scenarios. Traditional planning algorithms typically account for all traffic p
Accurate localization plays an important role in high-level autonomous driving systems. Conventional map matching-based localization methods solve the poses by explicitly matching map elements with sensor observations, generally sensitive to perception noise, therefore requiring costly hyperparamete
In autonomous driving, accurately identifying traffic participants that may influence vehicle behavior is crucial for effective system planning. To address this challenge, we propose a fault-tolerant mechanism for detecting action-induced objects, which significantly improves decision-making perform…
The vehicle dynamics model serves as a vital component of autonomous driving systems, as it describes the temporal changes in vehicle state. Traditional physics-based methods employ mathematical formulae to model vehicle dynamics, but they are unable to adequately describe complex vehicle systems du
High-precision vehicle localization with commercial setups is a crucial technique for high-level autonomous driving tasks. As a newly emerged approach, monocular localization in LiDAR map achieves promising balance between cost and accuracy, but estimating pose by finding correspondences between suc
With the popularity of implicit neural representations, or neural radiance fields (NeRF), there is a pressing need for editing methods to interact with the implicit 3D models for tasks like post-processing reconstructed scenes and 3D content creation. While previous works have explored NeRF editing…