Improving autonomous driving safety with pop: A framework for accurate partially observed trajectory predictions
S Wang, Y Chen, J Cheng, X Mei, R ** Uncertainty-aware Point-wise Lidar Inertial Odometry
H Yao, X Zhang, G Sun, Y Liu, X Zhang… - 2024 IEEE/RSJ …, 2024 - ieeexplore.ieee.org
This paper proposes a map** uncertainty-aware point-wise Lidar Inertial Odometry (LIO),
which synthesizes the point-wise point-to-plane match and map refreshment into a …
which synthesizes the point-wise point-to-plane match and map refreshment into a …
LHPF: Look back the History and Plan for the Future in Autonomous Driving
Decision-making and planning in autonomous driving critically reflect the safety of the
system, making effective planning imperative. Current imitation learning-based planning …
system, making effective planning imperative. Current imitation learning-based planning …
Safe and Real-Time Consistent Planning for Autonomous Vehicles in Partially Observed Environments via Parallel Consensus Optimization
L Zheng, R Yang, M Zheng, MY Wang, J Ma - arxiv preprint arxiv …, 2024 - arxiv.org
Ensuring safety and driving consistency is a significant challenge for autonomous vehicles
operating in partially observed environments. This work introduces a consistent parallel …
operating in partially observed environments. This work introduces a consistent parallel …