Autonomous Driving
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Benchmarks
Most implemented
YOLOX: Exceeding YOLO Series in 2021
PointPillars: Fast Encoders for Object Detection from Point Clouds
nuScenes: A multimodal dataset for autonomous driving
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Papers
Data-Driven Risk Fields for Safer End-to-End Autonomous Driving
Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities. Existing rule-based risk models provide interpretable safety priors, …
Autonomous DrivingCLFTv2: Efficient Camera-LiDAR Fusion for Semantic Segmentation via Hierarchical Feature Pyramids
Semantic segmentation for autonomous driving requires reliable detection of vulnerable road users (VRUs) despite heavy class imbalance. We introduce CLFTv2, a hierarchical camera-LiDAR fusion framework replacing global V…
Semantic SegmentationAutonomous DrivingA Risk-Sensitive and Uncertainty-Aware Decision-Making and Control Framework for Safe and Robust Autonomous Driving
Reinforcement learning (RL) has demonstrated considerable potential for autonomous driving decision-making. However, its deployment in urban autonomous driving, particularly at highly interactive unsignalized intersectio…
Reinforcement LearningAutonomous DrivingPlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving Systems (ADSs), but it remains a fragmented modular pipeline in which …
Autonomous DrivingOne Diffusion Model, Two Roles: Guided Trajectory Planning and Safety-Critical Scenario Generation in Closed-Loop Simulation
Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future trajectories in driving scenes. We show that a single pretrained diffusion traffic model can serve two complementa…
Trajectory PlanningAutonomous DrivingMotion PlanningMitigating Performance Discrepancy in Cross-Domain 3D Class-Incremental Learning
3D perception plays a crucial role in real-world applications such as autonomous driving, robotics, and AR/VR. In practical scenarios, 3D perception models need to continually adapt to newly emerging 3D object categories…
class-incremental learningAutonomous DrivingPoint Clouds