CFRLI-IDM: A Counterfactual Risk Level Inference Based Intelligence Driver Model for Extremely Aggressive Cut-in Scenario in China

Yongqiang Li, Yang Lv, Quan Wang, Qiankun Miao

2023

Abstract

When conducting unmanned delivery tasks on side roads in China, unmanned delivery vehicles sometimes face a dual challenge of aggressive cut-ins and reckless followers driving closely behind them. To address this challenge, we propose a cut-in response strategy named Counterfactual Risk Level Inference-based Intelligence Driver Model (CFRLI-IDM). The CFRLI-IDM method utilizes an improved Intelligent Driver Model (IDM) as the initial longitudinal control strategy for the ego vehicle. It then leverages counterfactual inference to construct an optimization problem, aiming to derive a longitudinal control strategy that satisfies the ego vehicle’s risk threshold constraint while maximizing compliance with the rear vehicle’s maximum acceptable braking deceleration constraint, with minimal changes to the initial strategy. To evaluate the effectiveness of our proposed method, we design an extremely challenging cut-in simulation scenario incorporating the aforementioned factors and validate the algorithm within this simulated environment. Experimental results demonstrate that our method prioritizes the safety of the ego vehicle while ensuring the safety of the rear vehicle as much as possible, substantially reducing the likelihood of safety accidents occurring in such complex scenarios.

Paper Citation

in Harvard Style

Li Y., Lv Y., Wang Q. and Miao Q. (2023). CFRLI-IDM: A Counterfactual Risk Level Inference Based Intelligence Driver Model for Extremely Aggressive Cut-in Scenario in China. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 273-280. DOI: 10.5220/0012209900003543

in Bibtex Style

@conference{icinco23,
author={Yongqiang Li and Yang Lv and Quan Wang and Qiankun Miao},
title={CFRLI-IDM: A Counterfactual Risk Level Inference Based Intelligence Driver Model for Extremely Aggressive Cut-in Scenario in China},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={273-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012209900003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - CFRLI-IDM: A Counterfactual Risk Level Inference Based Intelligence Driver Model for Extremely Aggressive Cut-in Scenario in China
SN - 978-989-758-670-5
AU - Li Y.
AU - Lv Y.
AU - Wang Q.
AU - Miao Q.
PY - 2023
SP - 273
EP - 280
DO - 10.5220/0012209900003543
PB - SciTePress