SOTAVerified

Autonomous Driving

Autonomous driving is the task of driving a vehicle without human conduction.

Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.

(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)

Papers

Showing 13511375 of 6092 papers

TitleStatusHype
DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving AgentsCode1
Domain Adaptation Through Task DistillationCode1
Multi-Echo Denoising in Adverse WeatherCode1
Domain Adaptation on Point Clouds via Geometry-Aware ImplicitsCode1
Domain Adaptive Object Detection for Autonomous Driving under Foggy WeatherCode1
DRAMA-X: A Fine-grained Intent Prediction and Risk Reasoning Benchmark For DrivingCode1
Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous DrivingCode1
Chirp Delay-Doppler Domain Modulation: A New Paradigm of Integrated Sensing and Communication for Autonomous VehiclesCode1
DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-drivingCode1
Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy WeatherCode1
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior PredictionCode1
Multiscale Domain Adaptive YOLO for Cross-Domain Object DetectionCode1
Does Thermal data make the detection systems more reliable?Code1
Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object DetectorsCode1
Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep LearningCode1
Multi-task Learning for Camera CalibrationCode1
Domain Adaptation In Reinforcement Learning Via Latent Unified State RepresentationCode1
Dream to Drive with Predictive Individual World ModelCode1
N-Agent Ad Hoc TeamworkCode1
PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous DrivingCode1
NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform RepresentationCode1
NEAT: Neural Attention Fields for End-to-End Autonomous DrivingCode1
NeFSAC: Neurally Filtered Minimal SamplesCode1
Deep Federated Learning for Autonomous DrivingCode1
Distribution-Aware Continual Test-Time Adaptation for Semantic SegmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ReasonNetDriving Score79.95Unverified
2InterFuserDriving Score76.18Unverified
3TCPDriving Score75.14Unverified
4TF++ WPDriving Score66.32Unverified
5Learning From All Vehicles (LAV)Driving Score61.85Unverified
6TransFuserDriving Score61.18Unverified
7TransFuser (Reproduced)Driving Score55.04Unverified
8TCP (Reproduced)Driving Score47.91Unverified
9Latent TransFuserDriving Score45.2Unverified
10GRIADDriving Score36.79Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC69.17Unverified
2TransFuserRC56.36Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC86.91Unverified
2TransFuserRC78.41Unverified