Selected work

Representative Papers

Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data

Jul 21, 2026

This study addresses the insufficient accuracy and reliability of energy consumption prediction for electric trucks by proposing a physics-informed modeling framework that integrates first-principles physical knowledge with data-driven techniques. The approach embeds fundamental energy loss mechanisms into machine learning architectures and leverages an ensemble of models—including Bayesian linear regression, neural networks, and gradient-boosted regression trees—to achieve both high-fidelity point predictions and robust uncertainty quantification. Experimental results demonstrate that the proposed method significantly outperforms conventional purely data-driven models in both predictive accuracy and uncertainty estimation, thereby validating the effectiveness and superiority of physics-guided feature modeling for energy consumption forecasting in electric freight transport.

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A Causal Probabilistic Framework for Perception-Informed Closed-Loop Simulation of Autonomous Driving

Jun 05, 2026

This study addresses the over-optimistic safety assessment of autonomous driving systems in conventional software-in-the-loop simulation, which typically assumes ideal perception and neglects environmental disturbances such as fog or rain that induce perceptual errors. To bridge this gap, the authors propose a perception-driven simulation framework integrating a causal probabilistic model to systematically inject physically plausible perception failures into standard scenario-based simulation pipelines. Grounded in the SOTIF (ISO 21448) validation framework, this approach effectively reproduces real-world perception failures and uncovers safety-critical risks that traditional simulations often miss. The method offers a scalable, realistic closed-loop testing pathway for verifying SOTIF compliance in advanced driver assistance and autonomous driving systems.

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Understanding Conversational Patterns in Multi-agent Programming: A Case Study on Fibonacci Game Development

May 22, 2026

This study addresses the challenges of multi-agent collaboration in software engineering—such as role misalignment, unstable convergence, and error propagation—that hinder reliable code generation. It presents the first systematic evaluation of dual-role agent collaboration (designer and programmer) along three dimensions: efficiency, consistency, and effectiveness. The authors construct 12 dialogue systems by pairing seven open-source large language models (Gemma 2/3, LLaMA 3.2/3.3, DeepSeek-R1, MiniCPM, and Qwen3) and conduct a multidimensional analysis using BLEU, ROUGE, and compilation success rates. Results show that DeepSeek-R1 self-pairing converges to the correct solution stably from the first round, while LLaMA 3.2 and Qwen3 self-pairings exhibit strong role alignment but deviate from correctness; all other pairings fail to converge effectively. This work provides a quantifiable framework and empirical insights for evaluating multi-agent programming collaboration.

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Practical validation of synthetic pre-crash scenarios

May 06, 2026

The representativeness of synthetic pre-crash scenarios is crucial for assessing the safety impact of Driving Automation Systems through virtual simulations. However, a gap remains in the robust evaluation of synthetic pre-crash scenarios' practical equivalence to their real-world counterparts; that is, whether they are similar enough for the intended assessment purpose. Conventional significance testing is inadequate, as it focuses on detecting differences rather than establishing practical equivalence. This study addresses the research gap by extending our previous work on a Bayesian Region of Practical Equivalence (ROPE)-based equivalence testing framework by introducing a binning-based approach to define appropriate statistics and equivalence criteria. Two binning-based statistics are proposed to measure practically meaningful distributional differences between datasets in the context of safety impact assessment. The framework's applicability is demonstrated through a case study, which tests the practical equivalence of two synthetic rear-end pre-crash datasets with a previously developed reference dataset in the context of the safety impact assessment of an Automatic Emergency Braking system. The results show that the framework provides informative quantitative assessments of practical equivalence as well as diagnostic insights into the divergence of datasets. Although the demonstration focuses on rear-end pre-crash scenarios, the framework is generic and extensible to broader validation contexts, providing an interpretable and principled basis for practical equivalence assessment across diverse synthetic data applications.

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From Research to Practice: An Interactive Rapid Review of Autonomous Driving System Testing in Industry

May 01, 2026

This study addresses the challenges of deploying autonomous driving system testing in industrial settings, where open-world environments, complex scenarios, and the absence of standardized methodologies hinder the practical application of academic research. To bridge this gap, the work introduces a practitioner-driven interactive rapid review approach, engaging 21 automotive industry experts to systematically evaluate the applicability of 17 academic studies in real-world contexts, with a focus on end-to-end testing methods and test completeness. The analysis identifies 12 key challenges, providing in-depth examination of the two most representative issues. By offering empirical evidence and actionable insights, this research lays a foundation for developing testing frameworks that better align with industrial needs and accelerates the translation of academic advances into practice.

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Recent publications

Latest Papers

Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data

Jul 21, 2026

This study addresses the insufficient accuracy and reliability of energy consumption prediction for electric trucks by proposing a physics-informed modeling framework that integrates first-principles physical knowledge with data-driven techniques. The approach embeds fundamental energy loss mechanisms into machine learning architectures and leverages an ensemble of models—including Bayesian linear regression, neural networks, and gradient-boosted regression trees—to achieve both high-fidelity point predictions and robust uncertainty quantification. Experimental results demonstrate that the proposed method significantly outperforms conventional purely data-driven models in both predictive accuracy and uncertainty estimation, thereby validating the effectiveness and superiority of physics-guided feature modeling for energy consumption forecasting in electric freight transport.

0 citationsRead paper

A Causal Probabilistic Framework for Perception-Informed Closed-Loop Simulation of Autonomous Driving

Jun 05, 2026

This study addresses the over-optimistic safety assessment of autonomous driving systems in conventional software-in-the-loop simulation, which typically assumes ideal perception and neglects environmental disturbances such as fog or rain that induce perceptual errors. To bridge this gap, the authors propose a perception-driven simulation framework integrating a causal probabilistic model to systematically inject physically plausible perception failures into standard scenario-based simulation pipelines. Grounded in the SOTIF (ISO 21448) validation framework, this approach effectively reproduces real-world perception failures and uncovers safety-critical risks that traditional simulations often miss. The method offers a scalable, realistic closed-loop testing pathway for verifying SOTIF compliance in advanced driver assistance and autonomous driving systems.

0 citationsRead paper

Understanding Conversational Patterns in Multi-agent Programming: A Case Study on Fibonacci Game Development

May 22, 2026

This study addresses the challenges of multi-agent collaboration in software engineering—such as role misalignment, unstable convergence, and error propagation—that hinder reliable code generation. It presents the first systematic evaluation of dual-role agent collaboration (designer and programmer) along three dimensions: efficiency, consistency, and effectiveness. The authors construct 12 dialogue systems by pairing seven open-source large language models (Gemma 2/3, LLaMA 3.2/3.3, DeepSeek-R1, MiniCPM, and Qwen3) and conduct a multidimensional analysis using BLEU, ROUGE, and compilation success rates. Results show that DeepSeek-R1 self-pairing converges to the correct solution stably from the first round, while LLaMA 3.2 and Qwen3 self-pairings exhibit strong role alignment but deviate from correctness; all other pairings fail to converge effectively. This work provides a quantifiable framework and empirical insights for evaluating multi-agent programming collaboration.

0 citationsRead paper

Practical validation of synthetic pre-crash scenarios

May 06, 2026

The representativeness of synthetic pre-crash scenarios is crucial for assessing the safety impact of Driving Automation Systems through virtual simulations. However, a gap remains in the robust evaluation of synthetic pre-crash scenarios' practical equivalence to their real-world counterparts; that is, whether they are similar enough for the intended assessment purpose. Conventional significance testing is inadequate, as it focuses on detecting differences rather than establishing practical equivalence. This study addresses the research gap by extending our previous work on a Bayesian Region of Practical Equivalence (ROPE)-based equivalence testing framework by introducing a binning-based approach to define appropriate statistics and equivalence criteria. Two binning-based statistics are proposed to measure practically meaningful distributional differences between datasets in the context of safety impact assessment. The framework's applicability is demonstrated through a case study, which tests the practical equivalence of two synthetic rear-end pre-crash datasets with a previously developed reference dataset in the context of the safety impact assessment of an Automatic Emergency Braking system. The results show that the framework provides informative quantitative assessments of practical equivalence as well as diagnostic insights into the divergence of datasets. Although the demonstration focuses on rear-end pre-crash scenarios, the framework is generic and extensible to broader validation contexts, providing an interpretable and principled basis for practical equivalence assessment across diverse synthetic data applications.

0 citationsRead paper

From Research to Practice: An Interactive Rapid Review of Autonomous Driving System Testing in Industry

May 01, 2026

This study addresses the challenges of deploying autonomous driving system testing in industrial settings, where open-world environments, complex scenarios, and the absence of standardized methodologies hinder the practical application of academic research. To bridge this gap, the work introduces a practitioner-driven interactive rapid review approach, engaging 21 automotive industry experts to systematically evaluate the applicability of 17 academic studies in real-world contexts, with a focus on end-to-end testing methods and test completeness. The analysis identifies 12 key challenges, providing in-depth examination of the two most representative issues. By offering empirical evidence and actionable insights, this research lays a foundation for developing testing frameworks that better align with industrial needs and accelerates the translation of academic advances into practice.

0 citationsRead paper