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 4001–4025 of 6092 papers

TitleStatusHype
End-to-End on-device Federated Learning: A case study—0
End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023—0
End-to-End Policy Gradient Method for POMDPs and Explainable Agents—0
Predictive Planner for Autonomous Driving with Consistency Models—0
End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning—0
End-To-End Training and Testing Gamification Framework to Learn Human Highway Driving—0
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering—0
End to End Video Segmentation for Driving : Lane Detection For Autonomous Car—0
Enduring, Efficient and Robust Trajectory Prediction Attack in Autonomous Driving via Optimization-Driven Multi-Frame Perturbation Framework—0
Energy-Efficient & Real-Time Computer Vision with Intelligent Skipping via Reconfigurable CMOS Image Sensors—0
EnergyShield: Provably-Safe Offloading of Neural Network Controllers for Energy Efficiency—0
Engineering Education in the Age of Autonomous Machines—0
Engineering Safety Requirements for Autonomous Driving with Large Language Models—0
Enhanced Automotive Object Detection via RGB-D Fusion in a DiffusionDet Framework—0
Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation—0
Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation—0
Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning—0
Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model—0
Enhancing Autonomous Driving Safety Analysis with Generative AI: A Comparative Study on Automated Hazard and Risk Assessment—0
Enhancing Autonomous Driving Safety through World Model-Based Predictive Navigation and Adaptive Learning Algorithms for 5G Wireless Applications—0
Enhancing Autonomous Driving Safety with Collision Scenario Integration—0
Evaluation and Improvement of Fault Detection for Large Language Models—0
Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning—0
Enhancing Interaction Modeling with Agent Selection and Physical Coefficient for Trajectory Prediction—0
Enhancing Lane Segment Perception and Topology Reasoning with Crowdsourcing Trajectory Priors—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