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 701–725 of 2605 papers

TitleStatusHype
Autonomous Cars: Vision based Steering Wheel Angle Estimation—0
Dependable Neural Networks for Safety Critical Tasks—0
Adaptive Target Tracking Using Retrospective Cost Input Estimation—0
DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar—0
Depth Estimation using Weighted-loss and Transfer Learning—0
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation—0
Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate—0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network—0
Designing a Recurrent Neural Network to Learn a Motion Planner for High-Dimensional Inputs—0
Designing Autonomous Vehicles: Evaluating the Role of Human Emotions and Social Norms—0
Deep Occupancy-Predictive Representations for Autonomous Driving—0
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving—0
Detecting Driveable Area for Autonomous Vehicles—0
Autonomous Vehicle Security: A Deep Dive into Threat Modeling—0
Detecting Unsigned Physical Road Incidents from Driver-View Images—0
Detection of Cyber-Attacks in Collaborative Intersection Control—0
Detection of Network and Sensor Cyber-Attacks in Platoons of Cooperative Autonomous Vehicles: a Sliding-Mode Observer Approach—0
Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles—0
Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention—0
Deterministic Guided LiDAR Depth Map Completion—0
Autonomous Vehicles that Alert Humans to Take-Over Controls: Modeling with Real-World Data—0
Developing Situational Awareness for Joint Action with Autonomous Vehicles—0
Development and Assessment of Autonomous Vehicles in Both Fully Automated and Mixed Traffic Conditions—0
Development of Simulation-based Lane Change Control System for Autonomous Vehicles—0
A Multicriteria Decision Making Approach to Study the Barriers to the Adoption of Autonomous Vehicles—0
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Benchmark Results

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