# Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models

arXiv: https://arxiv.org/abs/1701.08915 · HTML: https://mlsanity.com/paper/1701.08915/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2017.

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

- [A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation](https://mlsanity.com/paper/2112.00964/) (2021)
- [LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2501.15850/) (2025)
- [Achieving Efficiency in Black Box Simulation of Distribution Tails with Self-structuring Importance Samplers](https://mlsanity.com/paper/2102.07060/) (2021)
- [Continual Driving Policy Optimization with Closed-Loop Individualized Curricula](https://mlsanity.com/paper/2309.14209/) (2023)
- [Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems](https://mlsanity.com/paper/2111.02204/) (2021)
- [Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling](https://mlsanity.com/paper/2204.02351/) (2022)
- [CPSOR-GCN: A Vehicle Trajectory Prediction Method Powered by Emotion and Cognitive Theory](https://mlsanity.com/paper/2311.08086/) (2023)
- [A Real-time Critical-scenario-generation Framework for Testing Autonomous Driving System](https://mlsanity.com/paper/2206.00910/) (2022)

## Abstract (arXiv)

The process to certify highly Automated Vehicles has not yet been defined by any country in the world. Currently, companies test Automated Vehicles on public roads, which is time-consuming and inefficient. We proposed the Accelerated Evaluation concept, which uses a modified statistics of the surrounding vehicles and the Importance Sampling theory to reduce the evaluation time by several orders of magnitude, while ensuring the evaluation results are statistically accurate. In this paper, we further improve the accelerated evaluation concept by using Piecewise Mixture Distribution models, instead of Single Parametric Distribution models. We developed and applied this idea to forward collision control system reacting to vehicles making cut-in lane changes. The behavior of the cut-in vehicles was modeled based on more than 403,581 lane changes collected by the University of Michigan Safety Pilot Model Deployment Program. Simulation results confirm that the accuracy and efficiency of the Piecewise Mixture Distribution method outperformed single parametric distribution methods in accuracy and efficiency, and accelerated the evaluation process by almost four orders of magnitude.