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 2551–2575 of 2605 papers

TitleStatusHype
Detecting Adversarial Attacks on Neural Network Policies with Visual ForesightCode0
Cooperative Automated Vehicles: a Review of Opportunities and Challenges in Socially Intelligent Vehicles Beyond Networking—0
Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications—0
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps (Masters Thesis)—0
Towards Proving the Adversarial Robustness of Deep Neural Networks—0
Proceedings First Workshop on Formal Verification of Autonomous Vehicles—0
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous CarsCode0
Resilient Linear Classification: An Approach to Deal with Attacks on Training Data—0
Systematic Testing of Convolutional Neural Networks for Autonomous Driving—0
Designing Autonomous Vehicles: Evaluating the Role of Human Emotions and Social Norms—0
Enhanced Emotion Enabled Cognitive Agent Based Rear End Collision Avoidance Controller for Autonomous Vehicles—0
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model—0
Validation of Enhanced Emotion Enabled Cognitive Agent Using Virtual Overlay Multi-Agent System Approach—0
Benchmarking 6DOF Outdoor Visual Localization in Changing ConditionsCode0
Automated Generation of Diverse and Challenging Scenarios for Test and Evaluation of Autonomous Vehicles—0
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles—0
Wetness and Color From a Single Multispectral Image—0
Failing to Learn: Autonomously Identifying Perception Failures for Self-driving CarsCode0
Learning to Map Vehicles into Bird's Eye ViewCode0
End-to-end Learning of Image based Lane-Change DecisionCode0
Pedestrian Prediction by Planning using Deep Neural Networks—0
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning—0
LiDAR-Camera Calibration using 3D-3D Point correspondencesCode0
VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach—0
Counterfactual Multi-Agent Policy GradientsCode1
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Benchmark Results

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