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 53015325 of 6092 papers

TitleStatusHype
DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes0
Extraction and Assessment of Naturalistic Human Driving Trajectories from Infrastructure Camera and Radar Sensors0
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations0
Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios0
Shared Cross-Modal Trajectory Prediction for Autonomous Driving0
Physically Realizable Adversarial Examples for LiDAR Object Detection0
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic SegmentationCode1
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning0
Physical Model Guided Deep Image Deraining0
RetinaTrack: Online Single Stage Joint Detection and TrackingCode1
ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings0
Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior0
PiP: Planning-informed Trajectory Prediction for Autonomous DrivingCode1
Adversarial Attacks on Monocular Depth Estimation0
GISNet: Graph-Based Information Sharing Network For Vehicle Trajectory Prediction0
A Game-Theoretic Model of Human Driving and Application to Discretionary Lane-Changes0
End-to-end Autonomous Driving Perception with Sequential Latent Representation LearningCode1
Counterfactual Policy Evaluation for Decision-Making in Autonomous DrivingCode1
Explainable Object-induced Action Decision for Autonomous VehiclesCode1
Deep Constrained Q-learning0
3dDepthNet: Point Cloud Guided Depth Completion Network for Sparse Depth and Single Color Image0
Generalizable Pedestrian Detection: The Elephant In The Room0
Vision-Based Autonomous Driving: A Model Learning Approach0
Synthesize then Compare: Detecting Failures and Anomalies for Semantic SegmentationCode1
Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban RoadwaysCode1
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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