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A Deployment-Oriented Simulation Framework for Deep Learning-Based Lane Change Prediction
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AI
DOI：10.1109/LSP.2025.3638676.png)
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Abstract
En 中文
Advanced driving simulations are increasingly used in automated driving research, yet freely available data and tools remain limited. We present a new open-source framework for synthetic data generation for lane change (LC) intention recognition in highways. Built on the CARLA simulator, it advances the state-of-the-art by providing a 50-driver dataset, a large-scale 3D map, and code for reproducibility and new data creation. The 60 km highway map includes varying curvature radii and straight segments. The codebase supports simulation enhancements (traffic management, vehicle cockpit, engine noise) and Machine Learning (ML) model training and evaluation, including CARLA log post-processing into time series. The dataset contains over 3,400 annotated LC maneuvers with synchronized ego dynamics, road geometry, and traffic context. From an automotive industry perspective, we also assess leading-edge ML models on STM32 microcontrollers using deployability metrics. Unlike prior infrastructure-based works, we estimate time-to-LC from ego-centric data. Results show that a Transformer model yields the lowest regression error, while XGBoost offers the best trade-offs on extremely resource-constrained devices. The entire framework is publicly released to support advancement in automated driving research.
Keywords:
Data models
Vehicle dynamics
Transformers
Training
Time series analysis
Synthetic data
Long short term memory
Roads
Pipelines
Data collection
Automated driving functions
dataset
deep learning
driver assistance systems
simulation
time-to-lane-change prediction
transformer
XGBoost
Journal
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