A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in Adaptive Cruise Control

Abstract

Fully automated vehicles are expected to have a significant share of the road network traffic in the near future. Several commercial vehicles with full-range Adaptive Cruise Control (ACC) systems or semi-autonomous functionalities are already available on the market. Many research studies aim at leveraging the potential of automated driving in order to improve the fuel efficiency of vehicles. However, in the vast majority of those, fuel efficiency is isolated to the driving dynamics between a single follower-leader pair, hence overlooking the complex nature of traffic. Consequently fuel efficiency and the efficient use of the roadway capacity are framed as conflicting objectives, leading to fuel-economy control models that adopt highly conservative driving styles. This formulation of the problem could be seen as a user-optimal approach, where in spite of delivering savings for individual vehicles, there is the side-effect of the deterioration of traffic flow. An important point that is overlooked is that the inefficient use of roadway capacity gives rise to congested traffic and traffic breakdowns, which in return increases energy costs within the system. The optimisation methods used in these studies entail high computational costs and, therefore, impose a strict constraint on the scope of problem. In this study, the use of car-following models and the limitation of the search space of optimal strategies to the parameter space of these is proposed. The proposed framework enables performing much more comprehensive optimisations and conducting more extensive tests on the collective impacts of fuel-economy driving strategies. The results show that, as conjectured, a "short-sighted" user-optimal approach is unable to deliver overall fuel efficiency. Conversely, a system-optimal formulation for fuel efficient driving is presented, and it is shown that the objectives of fuel efficiency and traffic flow are in fact not only non-conflicting, but also that they could be viewed as one when the global benefits to the network are considered.

Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

“…The advantages of the CACC system include its potential to improve the string stability of vehicle platoons ( 28 ) and decrease the headway between vehicles ( 29 ). The effects of ACC systems on traffic safety ( 30 , 31 ) and fuel efficiency ( 32 ) have been the subject of many studies ( 33 ). However, the amount of total improvement has remained uncertain in studies focusing on various effects of CACC such as improving the string stability ( 29 ), improving traffic flow ( 34 ), decreasing the risk of rear-end collision ( 18 , 35 ), reducing fuel consumption ( 36 ), and reducing the average following gap in a vehicle platoon ( 37 ), with several simulations and modeling studies in which the assumptions limit the outcome of the studies.…”

Estimation and Analysis of Vehicle Exhaust Emissions at Signalized Intersections Using a Car-Following Model

“…Scholars have used the VT-Micro model [27,28] to conduct a series of studies on vehicle fuel consumption and emissions from different perspectives [29][30][31][32]. However, the VT-Micro model does not include the principles of engine operation and emissions, and the accuracy of the model depends on the resolution of the velocity-acceleration matrix.…”

Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making

“…Platoon-wide environmentfriendly CACC system was studied by Wang et al [42] and their objective assessment attained 2% fuel saving with 17% emission reductions. Mamouei et al [43] argued that fueleconomy based ACC control model would not lead to highly conservative driving dynamics of traffic.…”