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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1676–1700 of 2605 papers

TitleStatusHype
Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation—0
Survey and Systematization of 3D Object Detection Models and Methods—0
Object Detection in Autonomous Vehicles: Status and Open Challenges—0
Roadmap for Cybersecurity in Autonomous Vehicles—0
Ray Based Distributed Autonomous Vehicle Research Platform—0
Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems—0
Potential Game-Based Decision-Making for Autonomous Driving—0
Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management—0
Artificial Intelligence in Software Testing : Impact, Problems, Challenges and Prospect—0
Integration of Vehicular Clouds and Autonomous Driving: Survey and Future Perspectives—0
Driving Conflict Resolution of Autonomous Vehicles at Unsignalized Intersections: A Differential Game Approach—0
A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection—0
Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection—0
Emergence of Cascading Risk and Role of Spatial Locations of Collisions in Time-Delayed Platoon of Vehicles—0
Multi-Adversarial Safety Analysis for Autonomous Vehicles—0
Learning Based Task Offloading in Digital Twin Empowered Internet of Vehicles—0
Grounding Linguistic Commands to Navigable RegionsCode0
Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving—0
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions—0
Learning to integrate vision data into road network data—0
Resilient Branching MPC for Multi-Vehicle Traffic Scenarios Using Adversarial Disturbance Sequences—0
Human-vehicle Cooperative Visual Perception for Autonomous Driving under Complex Road and Traffic Scenarios—0
Cooperation for Scalable Supervision of Autonomy in Mixed Traffic—0
Formal Estimation of Collision Risks for Autonomous Vehicles: A Compositional Data-Driven Approach—0
Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios—0
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Benchmark Results

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