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 601625 of 6092 papers

TitleStatusHype
Experimental Comparison of Global Motion Planning Algorithms for Wheeled Mobile RobotsCode1
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic SystemsCode1
Empirical Performance Evaluation of Lane Keeping Assist on Modern Production VehiclesCode1
SeSame: Simple, Easy 3D Object Detection with Point-Wise SemanticsCode1
End-to-end Autonomous Driving Perception with Sequential Latent Representation LearningCode1
Efficient Visual Computing with Camera RAW SnapshotsCode1
Efficient Risk-Averse Reinforcement LearningCode1
Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and PriorsCode1
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance SystemsCode1
Efficient Baselines for Motion Prediction in Autonomous DrivingCode1
ECTLO: Effective Continuous-time Odometry Using Range Image for LiDAR with Small FoVCode1
Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVLCode1
Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference SpeedCode1
Edge Federated Learning Via Unit-Modulus Over-The-Air ComputationCode1
EDA: Evolving and Distinct Anchors for Multimodal Motion PredictionCode1
EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point CloudsCode1
Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality FusionCode1
Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation MapsCode1
EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous DrivingCode1
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object DiscoveryCode1
DVIS: Decoupled Video Instance Segmentation FrameworkCode1
DVI: Depth Guided Video Inpainting for Autonomous DrivingCode1
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative PerceptionCode1
Dynamic Conditional Imitation Learning for Autonomous DrivingCode1
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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