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 681–690 of 2605 papers

TitleStatusHype
A Multi-Scale Isolation Forest Approach for Real-Time Detection and Filtering of FGSM Adversarial Attacks in Video Streams of Autonomous Vehicles—0
A CASE STUDY ON OPTIMAL DEEP LEARNING MODEL FOR UAVS—0
Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management—0
Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model—0
Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation—0
Deep Reinforcement Learning for Adverse Garage Scenario Generation—0
A Multi-Modal States based Vehicle Descriptor and Dilated Convolutional Social Pooling for Vehicle Trajectory Prediction—0
Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception—0
Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study—0
Autonomous Driving and Residential Location Preferences: Evidence from a Stated Choice Survey—0
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Benchmark Results

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