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 4751–4775 of 6092 papers

TitleStatusHype
VISTREAM: Improving Computation Efficiency of Visual Streaming Perception via Law-of-Charge-Conservation Inspired Spiking Neural Network—0
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving—0
Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks—0
Visual Attention for Behavioral Cloning in Autonomous Driving—0
VisualBackProp for learning using privileged information with CNNs—0
Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving—0
Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze—0
Visual Saliency-Guided Channel Pruning for Deep Visual Detectors in Autonomous Driving—0
Visual Semantic Search: Retrieving Videos via Complex Textual Queries—0
Visual Sensor Pose Optimisation Using Visibility Models for Smart Cities—0
Visual SLAMMOT Considering Multiple Motion Models—0
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection—0
VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision—0
VLM-Assisted Continual learning for Visual Question Answering in Self-Driving—0
VLM-C4L: Continual Core Dataset Learning with Corner Case Optimization via Vision-Language Models for Autonomous Driving—0
VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion—0
VLMine: Long-Tail Data Mining with Vision Language Models—0
VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving—0
VLP: Vision Language Planning for Autonomous Driving—0
VL-SAFE: Vision-Language Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving—0
VL-UR: Vision-Language-guided Universal Restoration of Images Degraded by Adverse Weather Conditions—0
VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving—0
Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds—0
VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving—0
VSO: Visual Semantic Odometry—0
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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