Safety Challenges for Autonomous Vehicles in the Absence of Connectivity
Not on the map. Why?
Abstract (arXiv)
Autonomous vehicles (AVs) are promoted as a technology that will create a future with effortless driving and virtually no traffic accidents. AV companies claim that, when fully developed, the technology will eliminate 94% of all accidents that are caused by human error. These AVs will likely avoid the large number of crashes caused by impaired, distracted or reckless drivers. But there remains a significant proportion of crashes for which no driver is directly responsible. In particular, the absence of connectivity of an AV with its neighboring vehicles (V2V) and the infrastructure (I2V) leads to a lack of information that can induce such crashes. Since AV designs today do not require such connectivity, these crashes would persist in the future. Using prototypical examples motivated by the NHTSA pre-crash scenario typology, we show that fully autonomous vehicles cannot guarantee safety in the absence of connectivity. Combining theoretical models and empirical data, we also argue that such hazardous scenarios will occur with a significantly high probability. This suggests that incorporating connectivity is an essential step on the path towards safe AV technology.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jun 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
5 papers.
- 2021Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Reviewcited by 12
- 2022CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learningcited by 6
- 2025Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perceptioncited by 2
- 2024OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous Vehiclescited by 0
- 2026CooperDrive: Enhancing Driving Decisions Through Cooperative Perceptioncited by 0
- 2026CooperDrive: Enhancing Driving Decisions Through Cooperative Perception15 Apr 2026
- 2025Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception6 Jun 2025
- 2024OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous Vehicles9 Oct 2024
- 2022CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning31 Jan 2022
- 2021Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review16 Oct 2021