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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3901–3925 of 6092 papers

TitleStatusHype
ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction—0
Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments—0
Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations—0
Surround-View Vision-based 3D Detection for Autonomous Driving: A Survey—0
Provably Safe Reinforcement Learning with Step-wise Violation Constraints—0
EnergyShield: Provably-Safe Offloading of Neural Network Controllers for Energy Efficiency—0
Variational Voxel Pseudo Image TrackingCode0
Uncertainty-Aware AB3DMOT by Variational 3D Object DetectionCode0
Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and GoalsCode0
PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysis—0
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting—0
EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions—0
Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving—0
Domain Adaptation of Synthetic Driving Datasets for Real-World Autonomous Driving—0
Explainable Action Prediction through Self-Supervision on Scene Graphs—0
Uncertainty Calibration and its Application to Object DetectionCode0
State-wise Safe Reinforcement Learning: A Survey—0
Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approach—0
Physics Constrained Motion Prediction with Uncertainty Quantification—0
Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index—0
Development of Real-time Rendering Technology for High-Precision Models in Autonomous Driving—0
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver BehaviourCode0
Lidar Upsampling with Sliced Wasserstein Distance—0
Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition—0
Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Driving—0
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Benchmark Results

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