most citedHierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI20241 cited

Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning

Siyi Lu, Lei He, Shengbo Eben Li +3

End-to-end autonomous driving offers a streamlined alternative to the traditional modular pipeline, integrating perception, prediction, and planning within a single framework. Whil…

cs.CV2024

Unveiling the Black Box: Independent Functional Module Evaluation for Bird's-Eye-View Perception Model

Ludan Zhang, Xiaokang Ding, Yuqi Dai +2

End-to-end models are emerging as the mainstream in autonomous driving perception. However, the inability to meticulously deconstruct their internal mechanisms results in diminishe…

cs.CV20241 cited

Vision-Driven 2D Supervised Fine-Tuning Framework for Bird's Eye View Perception

Lei He, Qiaoyi Wang, Honglin Sun +5

Visual bird's eye view (BEV) perception, due to its excellent perceptual capabilities, is progressively replacing costly LiDAR-based perception systems, especially in the realm of…

cs.RO2024

DenserRadar: A 4D millimeter-wave radar point cloud detector based on dense LiDAR point clouds

Zeyu Han, Junkai Jiang, Xiaokang Ding +4

The 4D millimeter-wave (mmWave) radar, with its robustness in extreme environments, extensive detection range, and capabilities for measuring velocity and elevation, has demonstrat…

cs.AI2024

Feature Map Convergence Evaluation for Functional Module

Ludan Zhang, Chaoyi Chen, Lei He +1

Autonomous driving perception models are typically composed of multiple functional modules that interact through complex relationships to accomplish environment understanding. Howe…