# A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety

arXiv: https://arxiv.org/abs/1904.02697 · HTML: https://mlsanity.com/paper/1904.02697/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [Coverage based testing for V&V and Safety Assurance of Self-driving Autonomous Vehicles: A Systematic Literature Review](https://mlsanity.com/paper/2103.04364/) (2021)
- [Cooperation for Scalable Supervision of Autonomy in Mixed Traffic](https://mlsanity.com/paper/2112.07569/) (2021)
- [Safety in Traffic Management Systems: A Comprehensive Survey](https://mlsanity.com/paper/2308.06204/) (2023)
- [Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches](https://mlsanity.com/paper/2305.17941/) (2023)
- [Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety](https://mlsanity.com/paper/2503.00077/) (2025)
- [X-Blocks: Linguistic Building Blocks of Natural Language Explanations for Automated Vehicles](https://mlsanity.com/paper/2602.13248/) (2026)
- [CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Development](https://mlsanity.com/paper/2504.03989/) (2025)
- [Control Analysis and Design for Autonomous Vehicles Subject to Imperfect AI-Based Perception](https://mlsanity.com/paper/2509.12137/) (2025)
- [Approaches to Analysis and Design of AI-Based Autonomous Vehicles](https://mlsanity.com/paper/2509.12169/) (2025)

## Abstract (arXiv)

Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and techniques. However, while some researchers in this field believe AI is the core element to enhance safety, others believe AI imposes new challenges to assure the safety of these new AI-based systems and applications. In this non-convergent context, this paper presents a systematic literature review to paint a clear picture of the state of the art of the literature in AI on AV safety. Based on an initial sample of 4870 retrieved papers, 59 studies were selected as the result of the selection criteria detailed in the paper. The shortlisted studies were then mapped into six categories to answer the proposed research questions. An AV system model was proposed and applied to orient the discussions about the SLR findings. As a main result, we have reinforced our preliminary observation about the necessity of considering a serious safety agenda for the future studies on AI-based AV systems.