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 2061–2070 of 2605 papers

TitleStatusHype
Bridging the Gap between Optimal Trajectory Planning and Safety-Critical Control with Applications to Autonomous Vehicles—0
Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline—0
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving—0
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning—0
Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze—0
Impact of Disturbances on Mixed Traffic Control with Autonomous Vehicles—0
Reinforced Wasserstein Training for Severity-Aware Semantic Segmentation in Autonomous Driving—0
Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions—0
Robust and Scalable Techniques for TWR and TDoA based localization using Ultra Wide Band Radios—0
A Survey and Insights on Deployments of the Connected and Autonomous Vehicles in US—0
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Benchmark Results

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