Energy-Efficient Cooperative Adaptive Cruise Control Strategy using V2I

Jones, Stephen; Wikström, Niklas; Parrilla, Alejandro Ferreira; Patil, Renukadevi; Kural, Emre; Massoner, Alexander; Grauers, Anders

doi:10.1109/codit.2019.8820533

Cited by 10 publications

(10 citation statements)

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“…Along with ML, stochastic methods can also be used to model the system behaviour for a wide operating range. The most common statistical methods used in the ADAS field are the autoregressive models [43,44], both linear and nonlinear ones: Markov Chain and Hidden Markov Chain. They are effectively used for the prediction of the demanded torque/power [45,46], predecessor vehicle position [47], human driving errors [48], and lane changing maneuvers [49][50][51][52][53].…”

Section: Predictionmentioning

confidence: 99%

“…When some of the process states are not observable, the method is referred to as Hidden Markov Chain. In [44], short-term forecasts are performed by considering an autoregressive model that takes the previous speeds as input to estimate the vehicle future positions.…”

Section: Predictionmentioning

confidence: 99%

“…The objective of CACC is similar to the ACC aim, i.e., minimizing the tracking error on a predefined distance, maintaining the relative speed as close to zero as possible while reducing the acceleration rate (jerk). The preceding acceleration enters the formulation often as a disturbance of the system and can be handled by switching to the robust type of MPC [44,50,[108][109][110][111][112][113][114][115][116][117][118][119][120][121][122][123]. CACC could also consider the lateral dynamics and therefore lane keeping is performed, thus adding to the state variables the lateral position and velocity errors which are then minimized [114].…”

Section: Cost Function In Mpc Problemsmentioning

confidence: 99%

“…In these scenarios, inter-vehicular distance IVD is often bounded to be lower than a certain value (i.e., considering road occupancy for minimizing the impact on the traffic) and higher than a safety distance. Along with the distance, operational ranges are also provided to velocity, acceleration and acceleration rate to ensure passenger comfort [29,44,50,[90][91][92][93][94][95][96][97][98][99][100][101][102][103][104][108][109][110][111][112][113][114][115][116][117][118][119][120][121][122].…”

Section: Constraints In Mpc Problemsmentioning

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: Cost Function In Mpc Problemsmentioning

confidence: 99%

Section: Constraints In Mpc Problemsmentioning

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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“…Predictive Adaptive Cruise Control is an addition to the "traditional" ACC feature, mostly used as a term for commercial application, where in addition to the influence of the traffic ahead, also the geometry of the road is taken into account in order to adopt cruising speed within certain limits in order to minimize braking, maximize energy recuperation and save fuel in general. [5].…”

Section: Adaptive Cruise Controlco-simulation In the Cloudmentioning

confidence: 99%

Plataforma para desenvolvimento virtual e validação de aplicações em Direção Autônoma e RDE

Balic¹

2019

Blucher Engineering Proceedings

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A number of global drivers, including increasingly stringent safety and emissions standards, and the call for autonomous driving with a connected powertrain, demand that we find new methodologies for vehicle development. In order to reach development targets, both in terms of timeline and product attributes, and to master the complexity of vehicle variants, a common approach includes extensive usage of advanced simulation methods from the early concept phase all the way to the integration and system tests.For efficient frontloading of expensive prototype-based validation and its replacement with simulation/model-based development a number of challenges needs to be addressed.Advanced applications in area of ADAS* and RDE* require collaboration of different teams (powertrain, exhaust aftertreatment, thermal management, control function development, power electronics, vehicle dynamics, sensor integration, traffic environment etc.) in order to balance out often conflicting product attributes such as emission vs. driving pleasure, autonomous driving vs. safety etc. For capturing development scenarios in a virtual world, multi-domain simulation models, consisting of sub-models of different level of detail, different performance (even faster than the real time), from different vendors, in different tools and different environments (virtual and real) are necessary.The development and validation environment must ensure easy, fast and accurate integration and co-simulation of the models, as well as seamless transition from the virtual to the real world, automatized testing and optimization, consistent data handling along the entire process, and still be open for new models and new testing and evaluation techniques.This work presents and discusses real life examples and application scenarios of a consistent model-based development approach for ADAS applications (Platooning, Predictive and Adaptive Cruise Control etc.) and RDE development and validation (in the office and on the engine testbed), which combines advanced modelling and simulation technologies with lab, testbed and on-road tests.

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Review on the impacts of cooperative automated driving on transportation and environment

Zhang

Liu

Lei

et al. 2023

Transportation Research Part D: Transport and Environment

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Energy-Efficient Cooperative Adaptive Cruise Control Strategy using V2I

Cited by 10 publications

References 1 publication

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

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

Plataforma para desenvolvimento virtual e validação de aplicações em Direção Autônoma e RDE

Review on the impacts of cooperative automated driving on transportation and environment

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