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 1551–1575 of 6092 papers

TitleStatusHype
Online Distillation with Continual Learning for Cyclic Domain ShiftsCode0
GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian ManifoldsCode0
On a Formal Model of Safe and Scalable Self-driving CarsCode0
BEVScope: Enhancing Self-Supervised Depth Estimation Leveraging Bird's-Eye-View in Dynamic ScenariosCode0
OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban EnvironmentCode0
OKGR: Occluded Keypoint Generation and Refinement for 3D Object DetectionCode0
4D-CS: Exploiting Cluster Prior for 4D Spatio-Temporal LiDAR Semantic SegmentationCode0
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous DrivingCode0
BEV-LaneDet: a Simple and Effective 3D Lane Detection BaselineCode0
BEV-LaneDet: An Efficient 3D Lane Detection Based on Virtual Camera via Key-PointsCode0
ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object DetectionCode0
Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point CloudsCode0
Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous RacingCode0
An Adaptive Black-box Backdoor Detection Method for Deep Neural NetworksCode0
NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic SegmentationCode0
NumbOD: A Spatial-Frequency Fusion Attack Against Object DetectorsCode0
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformationsCode0
BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous DrivingCode0
nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving SystemsCode0
NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World ModelsCode0
Neuro-Symbolic Evaluation of Text-to-Video Models using Formal VerificationCode0
Benchmarking the Robustness of Optical Flow Estimation to CorruptionsCode0
Neural Observation Field Guided Hybrid Optimization of Camera PlacementCode0
NextStop: An Improved Tracker For Panoptic LIDAR Segmentation DataCode0
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous DrivingCode0
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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