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 921–930 of 2605 papers

TitleStatusHype
Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation—0
Powertrain Hybridization for Autonomous Vehicles—0
DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language ModelsCode2
MotionLM: Multi-Agent Motion Forecasting as Language ModelingCode1
Leveraging Topology for Domain Adaptive Road Segmentation in Satellite and Aerial Imagery—0
V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration—0
SEPT: Towards Efficient Scene Representation Learning for Motion Prediction—0
CoFiI2P: Coarse-to-Fine Correspondences for Image-to-Point Cloud Registration—0
Semantic Map Learning of Traffic Light to Lane Assignment based on Motion DataCode1
Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman FilterCode1
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Benchmark Results

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