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 851875 of 6092 papers

TitleStatusHype
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative PerceptionCode1
DVI: Depth Guided Video Inpainting for Autonomous DrivingCode1
Dynamic Conditional Imitation Learning for Autonomous DrivingCode1
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous DrivingCode1
Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous DrivingCode1
Linking vision and motion for self-supervised object-centric perceptionCode1
LiSu: A Dataset and Method for LiDAR Surface Normal EstimationCode1
Bezier Everywhere All at Once: Learning Drivable Lanes as Bezier GraphsCode1
LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDARCode1
Anchor-free Small-scale Multispectral Pedestrian DetectionCode1
LoLI-Street: Benchmarking Low-Light Image Enhancement and BeyondCode1
DualAD: Dual-Layer Planning for Reasoning in Autonomous DrivingCode1
Asymmetrical Bi-RNN for pedestrian trajectory encodingCode1
DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic FusionCode1
Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic SegmentationCode1
Dynamic Environment Prediction in Urban Scenes using Recurrent Representation LearningCode1
LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous DrivingCode1
M3D-RPN: Monocular 3D Region Proposal Network for Object DetectionCode1
M3Net: Multimodal Multi-task Learning for 3D Detection, Segmentation, and Occupancy Prediction in Autonomous DrivingCode1
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object DiscoveryCode1
DRL-Based Trajectory Tracking for Motion-Related Modules in Autonomous DrivingCode1
An Efficient Convex Hull-based Vehicle Pose Estimation Method for 3D LiDARCode1
BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal EstimationCode1
Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous DrivingCode1
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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