Beyond RMSE: Do Machine-Learned Models of Road User Interaction Produce Human-Like Behavior?

Srinivasan, Aravinda Ramakrishnan; Lin, Yi-Shin; Antonello, Morris; Knittel, Anthony; Hasan, Mohamed Noor; Hawasly, Majd; Redford, John; Ramamoorthy, Subramanian; Leonetti, Matteo; Billington, Jac; Romano, Richard; Markkula, Gustav

doi:10.1109/tits.2023.3263358

Cited by 6 publications

(4 citation statements)

References 44 publications

(56 reference statements)

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“…However, it should be noted that also with respect to this type of modeling, our results highlight the complexity of underlying mechanisms and behavioral phenomena that need to be learned and tested for. There is a mounting argument in favor of thoroughly investigating machine-learned behavior ( 56 ), and recent analyses of machine-learned road user models have indeed shown that the standard approach of training these, to minimize deviation between model-predicted and human trajectories, is not guaranteed to yield human-like interaction behavior ( 57 , 58 ). The mechanistic insights we have presented here, as well our approach of explicitly targeting interaction phenomena to be accounted for, may guide work towards more cognitively and behaviorally informed machine learning, to capture the subtleties of interaction that matter to humans.…”

Section: Discussionmentioning

confidence: 99%

Explaining human interactions on the road by large-scale integration of computational psychological theory

et al. 2023

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When humans share space in road traffic, as drivers or as vulnerable road users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but they need to be captured in models if automated vehicles are to coexist successfully with human road users. Empirical studies of human road user behavior implicate a large number of underlying cognitive mechanisms, which taken together are well beyond the scope of existing computational models. Here, we note that for all of these putative mechanisms, computational theories exist in different subdisciplines of psychology, for more constrained tasks. We demonstrate how these separate theories can be generalized from abstract laboratory paradigms and integrated into a computational framework for modeling human road user interaction, combining Bayesian perception, a theory of mind regarding others’ intentions, behavioral game theory, long-term valuation of action alternatives, and evidence accumulation decision-making. We show that a model with these assumptions—but not simpler versions of the same model—can account for a number of previously unexplained phenomena in naturalistic driver–pedestrian road-crossing interactions, and successfully predicts interaction outcomes in an unseen data set. Our modeling results contribute to demonstrating the real-world value of the theories from which we draw, and address calls in psychology for cumulative theory-building, presenting human road use as a suitable setting for work of this nature. Our findings also underscore the formidable complexity of human interaction in road traffic, with strong implications for the requirements to set on development and testing of vehicle automation.

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Section: Discussionmentioning

confidence: 99%

Explaining human interactions on the road by large-scale integration of computational psychological theory

et al. 2023

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“…More than one method was used to test the models prepared throughout the study. RMSE, a metric that was used to evaluate the accuracy of statistical predictions, was enacted in the initial testing processes [27]. RMSE was calculated as the square root of the mean square of the differences between the predicted values and the actual values (Equation ( 2)).…”

Section: Evaluation Metricsmentioning

confidence: 99%

Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles

Oyucu,

Doğan,

Aksöz

et al. 2024

Applied Sciences

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The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods’ performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies.

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“…More than one method was utilized to test the models prepared within the scope of this study. The RMSE, a metric employed to evaluate the accuracy of statistical predictions, was used in the initial testing processes [33]. The RMSE is calculated as the square root of the mean square of the differences between the predicted values and the actual values (Equation ( 2)):…”

Section: Evaluation Metricsmentioning

confidence: 99%

Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study

Oyucu,

Dümen,

Duru

et al. 2024

Symmetry

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Li-ion batteries are integral to various applications, ranging from electric vehicles to mobile devices, because of their high energy density and user friendliness. The assessment of the Li-ion state of heath stands as a crucial research domain, aiming to innovate safer and more effective battery management systems that can predict and promptly report any operational discrepancies. To achieve this, an array of machine learning (ML) and artificial intelligence (AI) methodologies have been employed to analyze data from Li-ion batteries, facilitating the estimation of critical parameters like state of charge (SoC) and state of health (SoH). The continuous enhancement of ML and AI algorithm efficiency remains a pivotal focus of scholarly inquiry. Our study distinguishes itself by separately evaluating traditional machine learning frameworks and advanced deep learning paradigms to determine their respective efficacy in predictive modeling. We dissected the performances of an assortment of models, spanning from conventional ML techniques to sophisticated, hybrid deep learning constructs. Our investigation provides a granular analysis of each model’s utility, promoting an informed and strategic integration of ML and AI in Li-ion battery state of health prognostics. Specifically, a utilization of machine learning algorithms such as Random Forests (RFs) and eXtreme Gradient Boosting (XGBoost), alongside regression models like Elastic Net and foundational neural network approaches including Multilayer Perceptron (MLP) were studied. Furthermore, our research investigated the enhancement of time series analysis using intricate models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and their outcomes with those of hybrid models, including a RNN-long short-term memory (LSTM), CNN-LSTM, CNN-Gated Recurrent Unit (GRU) and RNN-GRU. Comparative evaluations reveal that the RNN-LSTM configuration achieved a Mean Squared Error (MSE) of 0.043, R-Squared of 0.758, Root Mean Square Error (RMSE) of 0.208, and Mean Absolute Error (MAE) of 0.124, whereas the CNN-LSTM framework reported an MSE of 0.039, R-Squared of 0.782, RMSE of 0.197, and MAE of 0.122, underscoring the potential of deep learning-based hybrid models in advancing the accuracy of battery state of health assessments.

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Beyond RMSE: Do Machine-Learned Models of Road User Interaction Produce Human-Like Behavior?

Abstract: This is a repository copy of Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?.

Cited by 6 publications

References 44 publications

Explaining human interactions on the road by large-scale integration of computational psychological theory

Explaining human interactions on the road by large-scale integration of computational psychological theory

Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles

Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study

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