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 1241–1250 of 2605 papers

TitleStatusHype
Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty PredictionsCode1
Driving Style Recognition at First Impression for Online Trajectory Prediction—0
Uncertainty in Real-Time Semantic Segmentation on Embedded SystemsCode0
Fake it, Mix it, Segment it: Bridging the Domain Gap Between Lidar Sensors—0
Autonomous driving using GA-optimized neural network based adaptive LPV-MPC controllerCode1
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving—0
Predicting Autonomous Vehicle Collision Injury Severity Levels for Ethical Decision Making and Path Planning—0
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems—0
Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles—0
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving—0
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Benchmark Results

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