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 401–410 of 2605 papers

TitleStatusHype
FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye CameraCode1
Few-Shot Testing of Autonomous Vehicles with Scenario Similarity Learning—0
Lidar Panoptic Segmentation in an Open WorldCode1
DAP-LED: Learning Degradation-Aware Priors with CLIP for Joint Low-light Enhancement and Deblurring—0
SSE: Multimodal Semantic Data Selection and Enrichment for Industrial-scale Data Assimilation—0
VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle RobustnessCode0
Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making FrameworkCode2
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning—0
Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering—0
Safety Verification and Navigation for Autonomous Vehicles based on Signal Temporal Logic Constraints—0
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Benchmark Results

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