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 226–250 of 6092 papers

TitleStatusHype
Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous DrivingCode2
TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving ScenesCode2
RoGs: Large Scale Road Surface Reconstruction with Meshgrid GaussianCode2
ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous VehiclesCode2
RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging RadarCode2
Multi-Space Alignments Towards Universal LiDAR SegmentationCode2
A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic SegmentationCode2
Deep Learning-Based Point Cloud Registration: A Comprehensive Survey and TaxonomyCode2
SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth EstimationCode2
Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End SystemCode2
PRAM: Place Recognition Anywhere Model for Efficient Visual LocalizationCode2
Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?Code2
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong ElicitingCode2
Is CLIP the main roadblock for fine-grained open-world perception?Code2
LidarDM: Generative LiDAR Simulation in a Generated WorldCode2
HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view CamerasCode2
GraphAD: Interaction Scene Graph for End-to-end Autonomous DrivingCode2
Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous DrivingCode2
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous DrivingCode2
TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane SegmentationCode2
RCooper: A Real-world Large-scale Dataset for Roadside Cooperative PerceptionCode2
E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion DetectionCode2
MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation LearningCode2
Adaptive Fusion of Single-View and Multi-View Depth for Autonomous DrivingCode2
Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous DrivingCode2
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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