An LSTM-based Test Selection Method for Self-Driving Cars

2025-01-07

AI Summary

To address the low testing efficiency and difficulty in focusing on high-challenge scenarios in autonomous driving lane-keeping system validation, this paper proposes a test case selection method based on temporal modeling of road geometric features. For the first time, Long Short-Term Memory (LSTM) networks are introduced into autonomous driving test selection: key geometric attributes—such as curvature angle and segment length—are encoded as time-series sequences to train a binary deep learning classifier for identifying high-challenge test cases. Evaluation in a high-fidelity simulation environment demonstrates that the proposed method significantly outperforms conventional machine learning approaches in accuracy and precision, while achieving comparable recall and F1-score. These results empirically validate the efficacy of temporal modeling in enhancing test effectiveness. This work establishes a novel paradigm for functional testing of autonomous driving systems—geometric-feature-based temporal modeling—thereby advancing both methodology and practice in autonomous vehicle verification.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Self-driving cars require extensive testing, which can be costly in terms of time. To optimize this process, simple and straightforward tests should be excluded, focusing on challenging tests instead. This study addresses the test selection problem for lane-keeping systems for self-driving cars. Road segment features, such as angles and lengths, were extracted and treated as sequences, enabling classification of the test cases as"safe"or"unsafe"using a long short-term memory (LSTM) model. The proposed model is compared against machine learning-based test selectors. Results demonstrated that the LSTM-based method outperformed machine learning-based methods in accuracy and precision metrics while exhibiting comparable performance in recall and F1 scores. This work introduces a novel deep learning-based approach to the road classification problem, providing an effective solution for self-driving car test selection using a simulation environment.

Problem

• Autonomous Vehicles
• Lane Keeping System
• Efficient Testing Method

Innovation

• LSTM
• Autonomous Vehicles
• Deep Learning Testing Strategy