# The missing link: Developing a safety case for perception components in automated driving

arXiv: https://arxiv.org/abs/2108.13294 · HTML: https://mlsanity.com/paper/2108.13294/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges](https://mlsanity.com/paper/1905.04223/) (2019)
- [Waymo's Safety Methodologies and Safety Readiness Determinations](https://mlsanity.com/paper/2011.00054/) (2020)
- [Safety Case Templates for Autonomous Systems](https://mlsanity.com/paper/2102.02625/) (2021)
- [Self-driving car safety quantification via component-level analysis](https://mlsanity.com/paper/2009.01119/) (2020)

## Built on by (most cited first, 7 of 7)

- [Unaligned but Safe -- Formally Compensating Performance Limitations for Imprecise 2D Object Detection](https://mlsanity.com/paper/2202.05123/) (2022)
- [Logically Sound Arguments for the Effectiveness of ML Safety Measures](https://mlsanity.com/paper/2111.02649/) (2021)
- [STEAM & MoSAFE: SOTIF Error-and-Failure Model & Analysis for AI-Enabled Driving Automation](https://mlsanity.com/paper/2312.09559/) (2023)
- [A Safety Assurable Human-Inspired Perception Architecture](https://mlsanity.com/paper/2205.07862/) (2022)
- [Explaining Unreliable Perception in Automated Driving: A Fuzzy-based Monitoring Approach](https://mlsanity.com/paper/2505.14407/) (2025)
- [Detecting Trojaned DNNs via Spectral Regression Analysis](https://mlsanity.com/paper/2605.21146/) (2026)
- [Auditing Quality Filters for Long-Tail Human Data Curation](https://mlsanity.com/paper/2609.31896/) (2026)

## Abstract (arXiv)

Safety assurance is a central concern for the development and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine Learning (ML). Despite the known challenges with the safety assurance of ML-based components, proposals have recently emerged for unit-level safety cases addressing these components. Unfortunately, AD safety cases express safety requirements at the system level and these efforts are missing the critical linking argument needed to integrate safety requirements at the system level with component performance requirements at the unit level. In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument specifically tailored for perception components. The template takes a deductive and formal approach to define strong traceability between levels. We demonstrate the applicability of ISCaP with a detailed case study and discuss its use as a tool to support incremental development of perception components.