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 551–575 of 2605 papers

TitleStatusHype
HiMo: High-Speed Objects Motion Compensation in Point Clouds—0
Human-AI Interaction Design Standards—0
QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems—0
Dynamically Local-Enhancement Planner for Large-Scale Autonomous Driving—0
Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights—0
Constrained Generative Modeling with Manually Bridged Diffusion Models—0
CoopDETR: A Unified Cooperative Perception Framework for 3D Detection via Object Query—0
A Real-time Spatio-Temporal Trajectory Planner for Autonomous Vehicles with Semantic Graph Optimization—0
CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems—0
Between Innovation and Oversight: A Cross-Regional Study of AI Risk Management Frameworks in the EU, U.S., UK, and China—0
Automatic Vehicle Detection using DETR: A Transformer-Based Approach for Navigating Treacherous Roads—0
IBURD: Image Blending for Underwater Robotic Detection—0
Cross-Model Transferability of Adversarial Patches in Real-time Segmentation for Autonomous DrivingCode0
A Multi-Scale Isolation Forest Approach for Real-Time Detection and Filtering of FGSM Adversarial Attacks in Video Streams of Autonomous Vehicles—0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments—0
BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory PredictionCode0
Deep learning based infrared small object segmentation: Challenges and future directions—0
Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration—0
Multi-Objective Reinforcement Learning for Critical Scenario Generation of Autonomous Vehicles—0
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing—0
Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks—0
AI Generations: From AI 1.0 to AI 4.0—0
Safe platooning control of connected and autonomous vehicles on curved multi-lane roads—0
V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multi-Modal Large Language Models—0
Mind the Gaps: Logical English, Prolog, and Multi-agent Systems for Autonomous Vehicles—0
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Benchmark Results

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