Data to 7 Oct 2026

Computing Systems for Autonomous Driving: State-of-the-Art and Challenges

arXiv 2009.14349 ↗First posted 30 Sep 2020cs.DC cs.ROcited by 47

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Abstract (arXiv)

The recent proliferation of computing technologies (e.g., sensors, computer vision, machine learning, and hardware acceleration), and the broad deployment of communication mechanisms (e.g., DSRC, C-V2X, 5G) have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors. The key to the success of these autonomous systems is making a reliable decision in real-time fashion. However, accidents and fatalities caused by early deployed autonomous vehicles arise from time to time. The real traffic environment is too complicated for current autonomous driving computing systems to understand and handle. In this paper, we present state-of-the-art computing systems for autonomous driving, including seven performance metrics and nine key technologies, followed by twelve challenges to realize autonomous driving. We hope this paper will gain attention from both the computing and automotive communities and inspire more research in this direction.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 10; 2024: 19; 2025: 7; 2026: 5.

47 papers.

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2023Dolphins: Multimodal Language Model for Drivingcited by 124
2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
2023Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimizationcited by 60
2025A Survey of World Models for Autonomous Drivingcited by 60
2023Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examplescited by 40
2021A Survey on Simulators for Testing Self-Driving Carscited by 26
2024Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attackcited by 26
2023Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviorscited by 25
2024A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Drivingcited by 17
2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
2024Mitigating Backdoor Attack by Injecting Proactive Defensive Backdoorcited by 10
2024Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technologycited by 9

35 more

2024Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutionscited by 7
2022Online V2X Scheduling for Raw-Level Cooperative Perceptioncited by 6
2022Understanding Time Variations of DNN Inference in Autonomous Drivingcited by 6
2024Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmapcited by 6
2024Spatio-temporal Prompting Network for Robust Video Feature Extractioncited by 5
2024Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Drivingcited by 4
2023Data and Knowledge for Overtaking Scenarios in Autonomous Drivingcited by 2
2024Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detectioncited by 2
2024Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steeringcited by 2
2022OLLIE: Derivation-based Tensor Program Optimizercited by 1
2023Intelligent machines work in unstructured environments by differential neuromorphic computingcited by 1
2024Learning Autonomous Race Driving with Action Mapping Reinforcement Learningcited by 1
2024DeepBaR: Fault Backdoor Attack on Deep Neural Network Layerscited by 1
2024AdaShadow: Responsive Test-time Model Adaptation in Non-stationary Mobile Environmentscited by 1
2025Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environmentcited by 1
2025SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injectionscited by 1
2025AIM: Software and Hardware Co-design for Architecture-level IR-drop Mitigation in High-performance PIMcited by 1
20214C: A Computation, Communication, and Control Co-Design Framework for CAVscited by 0
2022Memory based neural networks for end-to-end autonomous drivingcited by 0
2023Intelligent Multi-channel Meta-imagers for Accelerating Machine Visioncited by 0
2023On the fly Deep Neural Network Optimization Control for Low-Power Computer Visioncited by 0
2024Exploiting Regional Information Transformer for Single Image Derainingcited by 0
2024Partial train and isolate, mitigate backdoor attackcited by 0
2024Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantizationcited by 0
2024LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Modelscited by 0
2024The ADUULM-360 Dataset -- A Multi-Modal Dataset for Depth Estimation in Adverse Weathercited by 0
2024Edge Delayed Deep Deterministic Policy Gradient: efficient continuous control for edge scenarioscited by 0
2025A Robust Attack: Displacement Backdoor Attackcited by 0
2025MergeGuard: Efficient Thwarting of Trojan Attacks in Machine Learning Modelscited by 0
2025A Survey on Deep Multi-Task Learning in Connected Autonomous Vehiclescited by 0
2026Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectorscited by 0
2026Enhancing Predictability of Multi-Tenant DNN Inference for Autonomous Vehicles' Perceptioncited by 0
2026Bandwidth-adaptive Cloud-Assisted 360-Degree 3D Perception for Autonomous Vehiclescited by 0
2026Autonomous Systems Dependability in the era of AI: Design Challenges in Safety, Security, Reliability and Certificationcited by 0
2026End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systemscited by 0