Is your lidar placement optimized for 3d scene understanding?

Y Li, L Kong, H Hu, X Xu… - Advances in Neural …, 2025 - proceedings.neurips.cc
The reliability of driving perception systems under unprecedented conditions is crucial for
practical usage. Latest advancements have prompted increasing interest in multi-LiDAR …

CrossPrune: Cooperative pruning for camera–LiDAR fused perception models of autonomous driving

Y Lu, B Jiang, N Liu, Y Li, J Chen, Y Zhang… - Knowledge-Based …, 2024 - Elsevier
Deep neural network pruning is effective in enabling high-performance perception models to
be deployed on autonomous driving platforms with limited computation and memory …

UniDrive: Towards Universal Driving Perception Across Camera Configurations

Y Li, W Zheng, X Huang, K Keutzer - arxiv preprint arxiv:2410.13864, 2024 - arxiv.org
Vision-centric autonomous driving has demonstrated excellent performance with
economical sensors. As the fundamental step, 3D perception aims to infer 3D information …

Learning Shared RGB-D Fields: Unified Self-supervised Pre-training for Label-efficient LiDAR-Camera 3D Perception

X Xu, Y Li, T Zhang, J Yang… - arxiv preprint arxiv …, 2024 - arxiv.org
Constructing large-scale labeled datasets for multi-modal perception model training in
autonomous driving presents significant challenges. This has motivated the development of …

[CITARE][C] Predictive Motion Control Considering Body Attitude Constraints for Four in-Wheel Motor Vehicles

H Chu, Z Li, Q Kang, X Liu, B Gao, H Chen - Unmanned Systems, 2024 - World Scientific
In-wheel motor distributed-drive electric vehicles have the unique characteristic of being
able to control the torque of each wheel independently and accurately. However, when …