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
Weather-Aware Domain Adaptation for Street-View Weather Recognition
Adverse conditions such as rain, snow, fog, and dust remain challenging for camera-based perception in autonomous driving. We study multi-class weather recognition from street-view images under domain shift, where most a…
Autonomous DrivingDomain AdaptationMapLightning: Online Vectorized HD Map Construction with 1D Map Tokens
Online vectorized HD map construction is essential for scaling safe autonomous driving and requires accurate, real-time inference. Prior methods typically rely on dense bird's-eye-view (BEV) grids as the intermediate rep…
Online Vectorized HD Map ConstructionTrajectory PredictionAutonomous DrivingEnd-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems
Autonomous driving systems have become a central focus of intelligent transportation research, with End-to-End Learning and Modular Architectures offering two prominent design paradigms for their implementation. E2E Lear…
Autonomous DrivingEvaluating Physical Consistency and Plausibility in Generative Scenario Models for Autonomous Driving
Generative AI models are increasingly used for scenario generation in autonomous driving. While they can generate realistic-looking scenarios, they often provide limited transparency into learned representations and cons…
Autonomous DrivingTowards Reliable Vision-Language Models for Autonomous Driving
Vision-Language models (VLMs) are increasingly being explored in autonomous driving for tasks such as scene understanding, driving reasoning, decision-making, and end-to-end driving. As their role becomes more prominent,…
Scene UnderstandingAutonomous DrivingDyRAD: Radar Novel View Synthesis for Dynamic Driving Scenes
Reconstructing dynamic driving scenes from recorded sensor data supports closed-loop evaluation of autonomous driving systems by synthesizing observations beyond the original trajectory. Unlike cameras and LiDAR, radar m…
Novel View SynthesisAutonomous Driving