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 1491–1500 of 2605 papers

TitleStatusHype
A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception—0
Adversarial Patterns: Building Robust Android Malware Classifiers—0
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model—0
Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving—0
Self-Supervised Scene Flow Estimation with 4-D Automotive RadarCode1
Explaining RADAR features for detecting spoofing attacks in Connected Autonomous Vehicles—0
Deep Camera Pose Regression Using Pseudo-LiDAR—0
PanoFlow: Learning 360° Optical Flow for Surrounding Temporal UnderstandingCode1
Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature AggregationCode1
RONELDv2: A faster, improved lane tracking method—0
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Benchmark Results

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