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 5151–5175 of 6092 papers

TitleStatusHype
RGM: Reconstructing High-fidelity 3D Car Assets with Relightable 3D-GS Generative Model from a Single Image—0
RHRSegNet: Relighting High-Resolution Night-Time Semantic Segmentation—0
RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding—0
RIFT: Closed-Loop RL Fine-Tuning for Realistic and Controllable Traffic Simulation—0
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures—0
RING#: PR-by-PE Global Localization with Roto-translation Equivariant Gram Learning—0
RIS Beam Calibration for ISAC Systems: Modeling and Performance Analysis—0
Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning—0
Risk-averse Behavior Planning for Autonomous Driving under Uncertainty—0
Risk-Averse Trust Region Optimization for Reward-Volatility Reduction—0
Risk-Aware Autonomous Driving with Linear Temporal Logic Specifications—0
Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications—0
Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation—0
Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarios—0
Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving—0
Risk Measurement, Risk Entropy, and Autonomous Driving Risk Modeling—0
Risk of Stochastic Systems for Temporal Logic Specifications—0
Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path—0
RITA: Boost Driving Simulators with Realistic Interactive Traffic Flow—0
RLAD: Reinforcement Learning from Pixels for Autonomous Driving in Urban Environments—0
R-LiViT: A LiDAR-Visual-Thermal Dataset Enabling Vulnerable Road User Focused Roadside Perception—0
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression—0
RNGDet++: Road Network Graph Detection by Transformer with Instance Segmentation and Multi-scale Features Enhancement—0
RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features—0
ROA-BEV: 2D Region-Oriented Attention for BEV-based 3D Object—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