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

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Showing 291–300 of 2605 papers

TitleStatusHype
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
1BAAMA3DP22.85—Unverified
2GSNetA3DP20.21—Unverified