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

arXiv: https://arxiv.org/abs/2009.14349 · HTML: https://mlsanity.com/paper/2009.14349/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 30 of 47)

- [Dolphins: Multimodal Language Model for Driving](https://mlsanity.com/paper/2312.00438/) (2023)
- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization](https://mlsanity.com/paper/2304.11823/) (2023)
- [A Survey of World Models for Autonomous Driving](https://mlsanity.com/paper/2501.11260/) (2025)
- [Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples](https://mlsanity.com/paper/2307.10562/) (2023)
- [Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack](https://mlsanity.com/paper/2405.16134/) (2024)
- [A Survey on Simulators for Testing Self-Driving Cars](https://mlsanity.com/paper/2101.05337/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors](https://mlsanity.com/paper/2305.11239/) (2023)
- [A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving](https://mlsanity.com/paper/2405.01440/) (2024)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Mitigating Backdoor Attack by Injecting Proactive Defensive Backdoor](https://mlsanity.com/paper/2405.16112/) (2024)
- [Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology](https://mlsanity.com/paper/2406.00490/) (2024)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [Online V2X Scheduling for Raw-Level Cooperative Perception](https://mlsanity.com/paper/2202.06085/) (2022)
- [Understanding Time Variations of DNN Inference in Autonomous Driving](https://mlsanity.com/paper/2209.05487/) (2022)
- [Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap](https://mlsanity.com/paper/2404.00019/) (2024)
- [Spatio-temporal Prompting Network for Robust Video Feature Extraction](https://mlsanity.com/paper/2402.02574/) (2024)
- [Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving](https://mlsanity.com/paper/2405.01691/) (2024)
- [Data and Knowledge for Overtaking Scenarios in Autonomous Driving](https://mlsanity.com/paper/2305.19421/) (2023)
- [Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection](https://mlsanity.com/paper/2405.01108/) (2024)
- [Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering](https://mlsanity.com/paper/2409.12716/) (2024)
- [OLLIE: Derivation-based Tensor Program Optimizer](https://mlsanity.com/paper/2208.02025/) (2022)
- [Intelligent machines work in unstructured environments by differential neuromorphic computing](https://mlsanity.com/paper/2309.08835/) (2023)
- [Learning Autonomous Race Driving with Action Mapping Reinforcement Learning](https://mlsanity.com/paper/2406.14934/) (2024)
- [DeepBaR: Fault Backdoor Attack on Deep Neural Network Layers](https://mlsanity.com/paper/2407.21220/) (2024)
- [AdaShadow: Responsive Test-time Model Adaptation in Non-stationary Mobile Environments](https://mlsanity.com/paper/2410.08256/) (2024)
- [Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environment](https://mlsanity.com/paper/2503.17756/) (2025)
- [SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections](https://mlsanity.com/paper/2504.03089/) (2025)
- [AIM: Software and Hardware Co-design for Architecture-level IR-drop Mitigation in High-performance PIM](https://mlsanity.com/paper/2511.04321/) (2025)
- [4C: A Computation, Communication, and Control Co-Design Framework for CAVs](https://mlsanity.com/paper/2107.01142/) (2021)

## 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.