# Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVL

arXiv: https://arxiv.org/abs/2109.07960 · HTML: https://mlsanity.com/paper/2109.07960/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 16 Sep 2021.

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

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry](https://mlsanity.com/paper/1812.05389/) (2018)
- [Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study](https://mlsanity.com/paper/1912.00805/) (2019)
- [Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators](https://mlsanity.com/paper/2012.06822/) (2020)

## Built on by (most cited first, 8 of 8)

- [Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System](https://mlsanity.com/paper/2204.07874/) (2022)
- [Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing](https://mlsanity.com/paper/2203.12026/) (2022)
- [Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications](https://mlsanity.com/paper/2203.16225/) (2022)
- [Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?](https://mlsanity.com/paper/2410.11769/) (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)
- [Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles](https://mlsanity.com/paper/2305.18380/) (2023)
- [CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Development](https://mlsanity.com/paper/2504.03989/) (2025)
- [From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing](https://mlsanity.com/paper/2604.18918/) (2026)

## Abstract (arXiv)

With the growing capabilities of autonomous vehicles, there is a higher demand for sophisticated and pragmatic quality assurance approaches for machine learning-enabled systems in the automotive AI context. The use of simulation-based prototyping platforms provides the possibility for early-stage testing, enabling inexpensive testing and the ability to capture critical corner-case test scenarios. Simulation-based testing properly complements conventional on-road testing. However, due to the large space of test input parameters in these systems, the efficient generation of effective test scenarios leading to the unveiling of failures is a challenge. This paper presents a study on testing pedestrian detection and emergency braking system of the Baidu Apollo autonomous driving platform within the SVL simulator. We propose an evolutionary automated test generation technique that generates failure-revealing scenarios for Apollo in the SVL environment. Our approach models the input space using a generic and flexible data structure and benefits a multi-criteria safety-based heuristic for the objective function targeted for optimization. This paper presents the results of our proposed test generation technique in the 2021 IEEE Autonomous Driving AI Test Challenge. In order to demonstrate the efficiency and effectiveness of our approach, we also report the results from a baseline random generation technique. Our evaluation shows that the proposed evolutionary test case generator is more effective at generating failure-revealing test cases and provides higher diversity between the generated failures than the random baseline.