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 81–90 of 2605 papers

TitleStatusHype
On the Need for a Statistical Foundation in Scenario-Based Testing of Autonomous Vehicles—0
On the Design of Resilient Distributed Single Time-Scale Estimators: A Graph-Theoretic Approach—0
Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models—0
Real Time Semantic Segmentation of High Resolution Automotive LiDAR ScansCode1
Safe and Efficient CAV Lane Changing using Decentralised Safety Shields—0
Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Survey—0
Fault Detection and Human Intervention in Vehicle Platooning: A Multi-Model Framework—0
Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions—0
The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving—0
Federated Learning-based Semantic Segmentation for Lane and Object Detection in Autonomous Driving—0
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Benchmark Results

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