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 191–200 of 2605 papers

TitleStatusHype
AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking LotCode1
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road ScenesCode1
Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving CarsCode1
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed TrafficCode1
Deep Sensor Fusion with Pyramid Fusion Networks for 3D Semantic SegmentationCode1
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place RecognitionCode1
A Baseline for the Commands For Autonomous Vehicles ChallengeCode1
Detection-segmentation convolutional neural network for autonomous vehicle perceptionCode1
Density-invariant Features for Distant Point Cloud RegistrationCode1
BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous DrivingCode1
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Benchmark Results

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