Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation

Chen Xiong; Cheng Wang; Yuhang Liu; Zirui Wu; Ye Tian

cs.ROMar 8, 2026

Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation

Authors: Chen Xiong, Cheng Wang, Yuhang Liu, Zirui Wu, Ye Tian

Organizations: School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China · Guangdong Provincial Key Laboratory of Intelligent Transportation System, Sun Yat-sen University, Guangzhou 510275, China · Department of Civil and Environmental Engineering, The Pennsylvania State University, State College, PA 16802, USA · School of Traffic Engineering and the Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China

Abstract

In contemporary autonomous driving testing, virtual simulation has become an important approach due to its efficiency and cost effectiveness. However, existing methods usually rely on reinforcement learning to generate risky scenarios, making it difficult to efficiently learn realistic emergency behaviors. To address this issue, we propose a behavior guided method for generating high risk lane change scenarios. First, a behavior learning module based on an optimized sequence generative adversarial network is developed to learn emergency lane change behaviors from an extracted dataset. This design alleviates the limitations of existing datasets and improves learning from relatively few samples. Then, the opposing vehicle is modeled as an agent, and the road environment together with surrounding vehicles is incorporated into the operating environment. Based on the Recursive Proximal Policy Optimization strategy, the generated trajectories are used to guide the vehicle toward dangerous behaviors for more effective risk scenario exploration. Finally, the reference trajectory is combined with model predictive control as physical constraints to continuously optimize the strategy and ensure physical authenticity. Experimental results show that the proposed method can effectively learn high risk trajectory behaviors from limited data and generate high risk collision scenarios with better efficiency than traditional methods such as grid search and manual design.

Figures & tables

Fig. 2.: The GA-SeqGAN Training Framework

Fig. 4.: Method for Generating Risky Lane-Change Scenarios in Vehicle Dynamics

Fig. 5.: The Triggering Process of the Emergency Lane Change Strategy

$Loss$	$Seqgan$	$Seqgan\_GRU$	$Seqgan\_GRU\_Attention$
NLL	11.235	10.371	10.112

TABLE I: Dissolution experiment results

Fig. 6.: The Triggering Process of the Emergency Lane Change Strategy

$Loss$	$SS$	$PG-BLEU$	$Seqgan$	$GA\_Seqgan$
NLL	14.431	14.242	13.143	11.451
p-value	$<10^{-6}$	$<10^{-6}$	–	–

TABLE II: Comparative results of sequence generation performance

Fig. 7.: Training Reward Curve (Dark Line: Mean Reward)

Fig. 8.: TTC Variation with Spatial Exploration

Fig. 9.: (a) The Contribution of cos-sim to Vehicle Attack Strategy;(b) Emergency lane-change behavior of the attacking vehicle with and without behavior guidance

Fig. 10.: Analysis of Model Training Convergence Under Different Strategies

Fig. 11.: TTC variation with spatial exploration under different strategies

Fig. 12.: Comparison of generated data and original data X, V distributions (X and V denote the lateral displacement and longitudinal velocity of the vehicle, respectively.)

Fig. 13.: Analysis of the Impact of adding MPC on the VAA Model

Fig. 14.: Visualization of .emergency lane-change collision

Fig. 15.: (a) Comparison of collision success rates achieved by different methods; (b) Comparison of computational time required by different methods

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