Artificially Intelligent Active Safety Systems

Vidano, Trevor; Assadian, Francis; Gulati, Nihal

doi:10.1007/978-3-031-06780-8_8

Cited by 1 publication

(1 citation statement)

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“…An automated driving system (ADS), as defined in [1] (i.e., Levels 3 to 5), have the potential to reduce on-road collisions and fatalities. Many manufacturers today provide a wide variety of active safety systems that fall under SAE Level 2, where a part of the dynamic driving task is automated by the system [2]. Only a handful of companies in the United States have demonstrated city-scale Level 3 or Level 4 deployment of ADS [3,4].…”

Section: Introductionmentioning

confidence: 99%

Control Performance Requirements for Automated Driving Systems

Vidano,

Assadian

2024

Electronics

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This research investigates the development of risk-based performance requirements for the control of an automated driving system (ADS). The proposed method begins by determining the target level of safety for the virtual driver of an ADS. The underlying assumptions are informed by existing data. Next, geometric models of the road and vehicle are used to derive deterministic performance levels of the virtual driver. To integrate the risk and performance requirements seamlessly, we propose new definitions for errors associated with the planner, pose, and control modules. These definitions facilitate the derivation of stochastic performance requirements for each module, thus ensuring an overall target level of safety. Notably, these definitions enable real-time controller performance monitoring, thus potentially enabling fault detection linked to the system’s overall safety target. At a high level, this approach argues that the requirements for the virtual driver’s modules should be designed simultaneously. To illustrate this approach, this technique is applied to a research project available in the literature that developed an automated steering system for an articulated bus. This example shows that the method generates achievable performance requirements that are verifiable through experimental testing and highlights the importance in validating the underlying assumptions for effective risk management.

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Section: Introductionmentioning

confidence: 99%