# Scenario-Based Test Reduction and Prioritization for Multi-Module Autonomous Driving Systems

arXiv: https://arxiv.org/abs/2209.01546 · HTML: https://mlsanity.com/paper/2209.01546/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2022.

## Built on by (most cited first, 6 of 6)

- [Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach](https://mlsanity.com/paper/2311.07377/) (2023)
- [NeuroStrata: Harnessing Neurosymbolic Paradigms for Improved Design, Testability, and Verifiability of Autonomous CPS](https://mlsanity.com/paper/2502.12267/) (2025)
- [Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems](https://mlsanity.com/paper/2402.18393/) (2024)
- [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)
- [TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems](https://mlsanity.com/paper/2507.05932/) (2025)
- [From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing](https://mlsanity.com/paper/2604.18918/) (2026)

## Abstract (arXiv)

When developing autonomous driving systems (ADS), developers often need to replay previously collected driving recordings to check the correctness of newly introduced changes to the system. However, simply replaying the entire recording is not necessary given the high redundancy of driving scenes in a recording (e.g., keeping the same lane for 10 minutes on a highway). In this paper, we propose a novel test reduction and prioritization approach for multi-module ADS. First, our approach automatically encodes frames in a driving recording to feature vectors based on a driving scene schema. Then, the given recording is sliced into segments based on the similarity of consecutive vectors. Lengthy segments are truncated to reduce the length of a recording and redundant segments with the same vector are removed. The remaining segments are prioritized based on both the coverage and the rarity of driving scenes. We implemented this approach on an industry level, multi-module ADS called Apollo and evaluated it on three road maps in various regression settings. The results show that our approach significantly reduced the original recordings by over 34% while keeping comparable test effectiveness, identifying almost all injected faults. Furthermore, our test prioritization method achieves about 22% to 39% and 41% to 53% improvements over three baselines in terms of both the average percentage of faults detected (APFD) and TOP-K.