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 5876–5900 of 6092 papers

TitleStatusHype
Evaluating the Robustness of Off-Road Autonomous Driving Segmentation against Adversarial Attacks: A Dataset-Centric analysisCode0
Cross-Model Transferability of Adversarial Patches in Real-time Segmentation for Autonomous DrivingCode0
ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image EnhancementCode0
Brain-Inspired Deep Imitation Learning for Autonomous Driving SystemsCode0
Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving EnvironmentCode0
Evaluating the Impact of Flaky Simulators on Testing Autonomous Driving SystemsCode0
Towards Calibrated Losses for Adversarial Robust Reject Option ClassificationCode0
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver BehaviourCode0
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point CloudsCode0
Level 2 Autonomous Driving on a Single Device: Diving into the Devils of OpenpilotCode0
Learning to Drive in a DayCode0
Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation in Nighttime Semantic SegmentationCode0
RNN-based linear parameter varying adaptive model predictive control for autonomous drivingCode0
Vehicular Visible Light Positioning for Collision Avoidance and Platooning: A SurveyCode0
Attention-Guided Lidar Segmentation and Odometry Using Image-to-Point Cloud Saliency TransferCode0
Learning to Cluster for Proposal-Free Instance SegmentationCode0
Relevance Attack on DetectorsCode0
Learning to Adapt for StereoCode0
Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspectiveCode0
Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNNCode0
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous DrivingCode0
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point CloudCode0
Squeeze-SegNet: A new fast Deep Convolutional Neural Network for Semantic SegmentationCode0
S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous DrivingCode0
Intrinsic Dynamics-Driven Generalizable Scene Representations for Vision-Oriented Decision-Making ApplicationsCode0
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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