Eco-Driving of Connected and Automated Vehicle With Preceding Driver Behavior Prediction

Ozkan, Mehmet Fatih; Ma, Yao

doi:10.1115/1.4048108

Cited by 21 publications

(26 citation statements)

References 15 publications

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“…In the last several decades, within the exploitation of Artificial Intelligence (AI), ML applied to ADAS systems has started to be more and more common. Successful applications have been reported for vehicle velocity prediction [31,32], lane detection [33], ACC [34], ECO-ACC [35], lane changing detection [36] and EMS with V2x connectivity [37]. This widespread diffusion is mainly due to the algorithms' ability to properly predict and identify a wide range of behaviours.…”

Section: Predictionmentioning

confidence: 99%

“…Due to their peculiar capability of learning by previous system operations, these algorithms have received greater attention from the scientific community for application in the ADAS field [39][40][41][42]. Ozkan et al [35] used a Gated Recurrent Unit, a type of RNN, to predict the future behaviour of the preceding human-driven vehicle based on the recording of the last several seconds provided by sensors. Lee et al [36] used a CNN to improve the comfort and safety of an ACC.…”

Section: Predictionmentioning

confidence: 99%

“…Table 2 summarizes the key features of MiL approaches for the fuel economy assessment considered in this section. The analysis highlights how MiL testing is extensively used to preliminary estimate performance improvement of innovative MPC control architecture to enhance ADAS performances [35]. This kind of test should consider many vehicles belonging to different classes to properly highlight the correlation between the vehicle class and the fuel economy potential.…”

Section: Tests For Performance Evaluationmentioning

confidence: 99%

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A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

et al. 2021

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Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.

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Section: Predictionmentioning

confidence: 99%

Section: Predictionmentioning

confidence: 99%

Section: Tests For Performance Evaluationmentioning

confidence: 99%

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A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

et al. 2021

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“…In this section, the nonlinear model predictive control (NMPC) algorithm is introduced to solve the PACC due to the nonlinearity introduced by the cost function. The basic concept of NMPC is to utilize the numerical optimization techniques and the predictive model to derive a control input sequence that minimizes the specified cost function over a defined time horizon subject to the system constraints [15].…”

Section: B Nonlinear Model Predictive Control (Nmpc) Implementationmentioning

confidence: 99%

Personalized Adaptive Cruise Control and Impacts on Mixed Traffic

Ozkan

2021

2021 American Control Conference (ACC)

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This paper presents a personalized adaptive cruise control (PACC) design that can learn human driver behavior and adaptively control the semi-autonomous vehicle (SAV) in the car-following scenario, and investigates its impacts on mixed traffic. In mixed traffic where the SAV and humandriven vehicles share the road, the SAV's driver can choose a PACC tuning that better fits the driver's preferred driving strategies. The individual driver's preferences are learned through the inverse reinforcement learning (IRL) approach by recovering a unique cost function from the driver's demonstrated driving data that best explains the observed driving style. The proposed PACC design plans the motion of the SAV by minimizing the learned unique cost function considering the short preview information of the preceding human-driven vehicle. The results reveal that the learned driver model can identify and replicate the personalized driving behaviors accurately and consistently when following the preceding vehicle in a variety of traffic conditions. Furthermore, we investigated the impacts of the PACC with different drivers on mixed traffic by considering time headway, gap distance, and fuel economy assessments. A statistical investigation shows that the impacts of the PACC on mixed traffic vary among tested drivers due to their intrinsic driving preferences.

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“…Fuel being non-renewable energy source, it is crucial to find ways to reduce fuel consumption. Alternative fuel methods such as hybrid and electric vehicles are being tested but yet to become mainstream (Rios-Torres et al , 2019; Ozkan and Yao, 2021). Several vehicle design interventions like aerodynamic design, optimising engine and transmission efficiency, regenerative braking, automatic gearbox and cruise control are some of the advanced features available in a semi-autonomous car resulting in significant fuel saving.…”

Section: Introductionmentioning

confidence: 99%

A machine learning pipeline for fuel-economical driving model

Jain

Mittal

2021

IJICC

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PurposeA cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.Design/methodology/approachThis research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.FindingsA composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.Research limitations/implicationsThe proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.Practical implicationsThe suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.Originality/valueThis paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

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Eco-Driving of Connected and Automated Vehicle With Preceding Driver Behavior Prediction

Cited by 21 publications

References 15 publications

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Personalized Adaptive Cruise Control and Impacts on Mixed Traffic

A machine learning pipeline for fuel-economical driving model

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