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 701–750 of 2605 papers

TitleStatusHype
Deep Reinforcement Learning for Adverse Garage Scenario Generation—0
Dependable Neural Networks for Safety Critical Tasks—0
A Multi-Modal States based Vehicle Descriptor and Dilated Convolutional Social Pooling for Vehicle Trajectory Prediction—0
Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception—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 Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study—0
Autonomous Driving and Residential Location Preferences: Evidence from a Stated Choice Survey—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 Radar Waveform Design for Efficient Automotive Radar Sensing—0
Deep Probability Estimation—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
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance—0
Autonomous Cars: Vision based Steering Wheel Angle Estimation—0
Adaptive Target Tracking Using Retrospective Cost Input Estimation—0
DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing—0
DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles—0
Diffusion-Based Environment-Aware Trajectory Prediction—0
Diffusion-Based Particle-DETR for BEV Perception—0
Digital Twins and Testbeds for Supporting AI Research with Autonomous Vehicle Networks—0
Analyze Drivers' Intervention Behavior During Autonomous Driving -- A VR-incorporated Approach—0
DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar—0
Directed-CP: Directed Collaborative Perception for Connected and Autonomous Vehicles via Proactive Attention—0
DISC: Dataset for Analyzing Driving Styles In Simulated Crashes for Mixed Autonomy—0
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation—0
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving—0
Deep Occupancy-Predictive Representations for Autonomous Driving—0
DISeR: Designing Imaging Systems with Reinforcement Learning—0
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving—0
Dissonance Between Human and Machine Understanding—0
Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles—0
DistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices—0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
Distributed Learning and Inference with Compressed Images—0
Distributed Observer Design for Tracking Platoon of Connected and Autonomous Vehicles—0
Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving—0
Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach—0
Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention—0
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Benchmark Results

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