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 6026–6050 of 6092 papers

TitleStatusHype
Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain AdaptationCode0
Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous DrivingCode0
A Robust and Reliable Point Cloud Recognition Network Under Rigid TransformationCode0
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is ComingCode0
AR-TTA: A Simple Method for Real-World Continual Test-Time AdaptationCode0
VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated LearningCode0
Dynamic Adversarial Attacks on Autonomous Driving SystemsCode0
Informed Reinforcement Learning for Situation-Aware Traffic Rule ExceptionsCode0
Dual Thinking and Logical Processing -- Are Multi-modal Large Language Models Closing the Gap with Human Vision ?Code0
Artificial Dummies for Urban Dataset AugmentationCode0
Increasing Data Efficiency of Driving Agent By World ModelCode0
Affordable Modular Autonomous Vehicle Development PlatformCode0
Unsupervised Adaptation from Repeated Traversals for Autonomous DrivingCode0
Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative PerceptionCode0
DR-WLC: Dimensionality Reduction cognition for object detection and pose estimation by Watching, Learning and CheckingCode0
Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection SystemCode0
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage GenerationCode0
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep NetworksCode0
Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approachCode0
Incorporating Luminance, Depth and Color Information by a Fusion-based Network for Semantic SegmentationCode0
Driving through the Concept Gridlock: Unraveling Explainability Bottlenecks in Automated DrivingCode0
DR.CPO: Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR OcclusionCode0
3D Multi-Object Tracking: A Baseline and New Evaluation MetricsCode0
Virtual to Real Reinforcement Learning for Autonomous DrivingCode0
Aerial Monocular 3D Object DetectionCode0
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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