SOTAVerified

Autonomous Vehicles

Autonomous vehicles is the task of making a vehicle that can guide itself 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: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )

Papers

Showing 276300 of 2605 papers

TitleStatusHype
Monitoring and Adapting the Physical State of a Camera for Autonomous VehiclesCode1
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?Code1
BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous DrivingCode1
A Shared Representation for Photorealistic Driving SimulatorsCode1
CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal RelationshipsCode1
CAVIAR: Co-simulation of 6G Communications, 3D Scenarios and AI for Digital TwinsCode1
I see you: A Vehicle-Pedestrian Interaction Dataset from Traffic Surveillance CamerasCode1
Learning Interpretable, High-Performing Policies for Autonomous DrivingCode1
Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?Code1
BAAM: Monocular 3D Pose and Shape Reconstruction With Bi-Contextual Attention Module and Attention-Guided ModelingCode1
Chirp Delay-Doppler Domain Modulation: A New Paradigm of Integrated Sensing and Communication for Autonomous VehiclesCode1
A Baseline for the Commands For Autonomous Vehicles ChallengeCode1
Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control ApproachCode1
Collaborative Motion Prediction via Neural Motion Message PassingCode1
CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy TrafficCode1
iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement LearningCode1
ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based DataCode1
Lane Graph Estimation for Scene Understanding in Urban DrivingCode1
Continual Driving Policy Optimization with Closed-Loop Individualized CurriculaCode1
COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked VehiclesCode1
Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehiclesCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement LearningCode1
CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-AttentionCode1
Automated Lane Change via Adaptive Interactive MPC: Human-in-the-Loop ExperimentsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BAAMA3DP22.85Unverified
2GSNetA3DP20.21Unverified