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 5351–5375 of 6092 papers

TitleStatusHype
Deep Neural Network Perception Models and Robust Autonomous Driving Systems—0
DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular VideosCode1
RODNet: Radar Object Detection Using Cross-Modal SupervisionCode1
Analyzing Accuracy Loss in Randomized Smoothing Defenses—0
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization—0
Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification—0
Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method—0
MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic NavigationCode1
Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D GeometryCode1
ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object DetectionCode1
FLIC: Fast Lidar Image Clustering—0
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial RelationshipsCode1
3D Point Cloud Processing and Learning for Autonomous Driving—0
An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice—0
HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection—0
Human-Machine Collaboration for Automated Vehicles via an Intelligent Two-Phase Haptic Interface—0
Training Adversarial Agents to Exploit Weaknesses in Deep Control PoliciesCode0
TGGLines: A Robust Topological Graph Guided Line Segment Detector for Low Quality Binary Images—0
Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking—0
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs—0
3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic SegmentationCode1
Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approachCode0
Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-driving ImagesCode1
SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances—0
Spatiotemporal Relationship Reasoning for Pedestrian Intent PredictionCode1
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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