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 721–730 of 2605 papers

TitleStatusHype
Autonomous Vehicles that Alert Humans to Take-Over Controls: Modeling with Real-World Data—0
Developing Situational Awareness for Joint Action with Autonomous Vehicles—0
Development and Assessment of Autonomous Vehicles in Both Fully Automated and Mixed Traffic Conditions—0
Development of Simulation-based Lane Change Control System for Autonomous Vehicles—0
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance—0
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving—0
Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement—0
DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing—0
DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles—0
Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles—0
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Benchmark Results

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