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 1431–1440 of 2605 papers

TitleStatusHype
READ: Large-Scale Neural Scene Rendering for Autonomous DrivingCode2
An Objective Method for Pedestrian Occlusion Level Classification—0
Designing a Recurrent Neural Network to Learn a Motion Planner for High-Dimensional Inputs—0
The Impact of Partial Occlusion on Pedestrian DetectabilityCode0
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation—0
Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles—0
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation—0
Multi-Agent Deep Reinforcement Learning in Vehicular OCC—0
Neuroevolutionary Multi-objective approaches to Trajectory Prediction in Autonomous Vehicles—0
COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked VehiclesCode1
Show:102550
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Benchmark Results

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