Energy-Efficient Cooperative Adaptive Cruise Control with Receding
                    Horizon of Traffic, Route Topology, and Traffic Light
                    Information

Wikström, Niklas; Parrilla, Alejandro Ferreira; Jones, Stephen J.; Grauers, Anders

doi:10.4271/12-02-02-0006

Cited by 9 publications

(4 citation statements)

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“…The above-described methods can also be combined one to another to overcome single limitations. For example, Wikström et al [62] used two different methods for predicting a short-time horizon and a long-time one. The former is referred to by using a stochastic approach whereas the latter is addressed to by exploiting the information from the TL infrastructure, which is in turn used to predict the predecessor vehicle behaviour.…”

Section: Predictionmentioning

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%

“…A reduction in energy consumption in the range of 13-23 % was obtained together with a reduction in the overall DP performance by 2-7%. Detailed scenarios that include dynamic traffic elements, such as traffic lights, also require careful analysis [62] and might lead to fuel economy potential from 5.2 to 29.8%.…”

Section: Tests For Performance Evaluationmentioning

confidence: 99%

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

et al. 2021

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“…Nowadays, more researches focus on CACC systems with both V2V and I2V technologies. Wikstro¨m and Jones 127,128 presented an energy efficient CACC strategy leveraging both V2V and I2V communications in an MPC framework. Traffic Lights Assignment (TLA) is also considered in the study.…”

Section: Acc With Connected Vehicle Technologiesmentioning

confidence: 99%

Researches on Adaptive Cruise Control system: A state of the art review

Wang

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Adaptive Cruise Control (ACC) is one of Advanced Driver Assistance Systems (ADAS) which takes over vehicle longitudinal control under necessary driving scenarios. Vehicle in ACC mode automatically adjusts speed to follow the preceding vehicle based on evaluation of the surrounding traffic. ACC reduces drivers’ workload as well as improves driving safety, energy economy, and traffic flow. This article provides a comprehensive review of the researches on ACC. Firstly, an overview of ACC controller and applied control theories are introduced. Their principles and performances are discussed. Secondly, several application cases of ACC control algorithms are presented. Then validation work including simulation, Hardware-in-the-Loop (HiL) test and on-road experiment is descripted to provide ideas for testing ACC systems for different aims and fidelities. In addition, studies on human-machine interaction are also summarized in this review to provide insights on development of ACC from the perspective of users. At last, challenges and potential directions in this field is discussed, including consideration of vehicle dynamics properties, contradiction between algorithm performance and computation as well as integration of ACC to other intelligent functions on vehicles.

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“…The majority of those are caused by humans [35], whose reaction times, distraction levels, tiredness or influence of (legal/illegal) performance altering substances, where those deficiencies could be prevented by automated vehicle controls. The complementary AD features contribute to improved traffic flow and increased road capacity, include applications, such as intelligent route-planning or platooning [36]. However, the positive benefits are matched by a possible lack of acceptance of the new technology [33], which is related *The work presented in this document has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements (Omitted for blinded review).…”

Section: Introductionmentioning

confidence: 99%

Measuring trust in automated driving using a multi-level approach to human factors

Clement

Danzinger

Veledar

et al. 2021

2021 24th Euromicro Conference on Digital System Design (DSD)

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As the driving is shifting towards automation, the maximization of related benefits would profit from improved user acceptance of the new technology. Studies suggest a strong connection between acceptance and trust in technical solutions. We investigate the improvement of user trust to driving automation through demonstrations that carried on a sophisticated driving simulator. The study correlates subjective data with objective psycho-physiological measurements. The multi-factorial and multivariate analysis of variance investigates the influence of learning effects and pre-experience with ADAS on trust. Results show improvement in trust through user interaction with a human-machine interface of the demonstrated AD system, hence illustrating the relevance of human-centered development processes. The conclusion is supported by the observation of driver cardiac signals.

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Energy-Efficient Cooperative Adaptive Cruise Control with Receding Horizon of Traffic, Route Topology, and Traffic Light Information

Cited by 9 publications

References 0 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

Researches on Adaptive Cruise Control system: A state of the art review

Measuring trust in automated driving using a multi-level approach to human factors

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