Search

• Commercial ACC systems have a string-unstable behavior. • String-stable ACCs rely on the use of communication which is not currently available. • ACC can still be string stable by taking advantage of RADAR sensing characteristics. • Monitoring two vehicles downstream is sufficient to ensure string-stability in a wide range of scenarios. • RADAR based multianticipation can provide a plug-and-play solution to achieve efficient traffic. Adaptive Cruise Control (ACC) systems have been expected to solve many problems of motorway traffic. Now that they are widespread, it is observed that the majority of existing systems are string unstable. Therefore, small perturbations in the speed profile of a vehicle are amplified for the vehicles following upstream, with negative impacts on traffic flow, fuel consumption, and safety. Increased headway settings provide more stable flow but at the same time it deteriorates the capacity. Substantial research has been carried out in the past decade on utilizing connectivity to overcome this trade-off. However, such connectivity solutions have to overcome several obstacles before deployment and there is the concrete risk that motorway traffic flow will considerably deteriorate in the meanwhile. As an alternative solution, the paper explores multianticipation without inter-vehicle communication, taking advantage of the recent advancements in the field of RADAR sensing. An analytical study is carried out, based on the most widely used model and parameter settings used to simulate currently available commercial ACC systems, comparing the transfer functions and step responses for the nominal and the multianticipative formulations. Then, a microsimulation framework is employed to validate our claim on different speed profiles. Analytical results demonstrate that multianticipation enhances stability without impacting traffic flow. On the contrary, the simulation study indicates that the multianticipative-ACC can produce higher road capacity even in the presence of external disturbances and for a wide range of calibrated parameters. Finally, optimality conditions for the tuning of the headway policy are derived from a Pareto optimization. [ABSTRACT FROM AUTHOR]

• Accuracy of ninety CF models and ACC algorithms is compared. • New controllers with spacing policies from the IDM and Gipps' models are studied. • The impact on accuracy of increasingly complex lower-level dynamics is evaluated. • The linear controller with Gipps' spacing policy is the most accurate model. • The IDM is the most robust model, also showing zero crashes in cross-validation. This paper investigates the accuracy and robustness of car-following (CF) and adaptive cruise control (ACC) models in reproducing measured trajectories of commercial ACCs. To this aim, a general modelling framework is proposed, in which ACC and CF models have been incrementally augmented with physics-based extensions: namely, perception delay, linear or nonlinear vehicle dynamics, and acceleration constraints. This framework has been applied to the Intelligent Driver Model (IDM), Gipps' model, and to three basic ACC algorithms. These are linear controllers which are coupled with a constant time-headway spacing policy, and with two other policies derived from the traffic flow theory: the IDM desired distance function, and Gipps' equilibrium distance-speed function. The ninety models resulting from the combination of the five base models with the aforementioned extensions, have been assessed and compared through a vast calibration and validation experiment against measured trajectory data of vehicles driven by ACC systems. Overall, the study has shown that physics-based extensions provide limited improvements to the accuracy of existing models. In addition, if an investigation against measured data is not carried out, it is not possible to argue which extension is the most suited for a specific model. The linear controller with Gipps' spacing policy has resulted the most accurate model, while the IDM the most robust to different input trajectories. Eventually, all models have failed to capture the behaviour of some car brands – just as models fail with some human drivers. Therefore, the choice of the "best" model is independent of the car brand to simulate. [ABSTRACT FROM AUTHOR]

• Adaptive Cruise Control (ACC) is an increasingly widespread technology in modern vehicles. • ACC can be considered a precursor of automated vehicles behavior on motorways. • There is still limited understanding of commercial ACC control logic. • Actual impact of ACC on motorway traffic dynamics is still widely unknown. • openACC aims to allow the scientific community to better understand ACC and its implications. Adaptive Cruise Control (ACC) systems are becoming increasingly available as a standard equipment in modern commercial vehicles. Their penetration rate in the fleet is constantly increasing, as well as their use, especially under freeway conditions. At the same time, limited information is openly available on how these systems actually operate and their differences depending on the vehicle manufacturer or model. This represents an important gap because as the number of ACC vehicles on the road increases, traffic dynamics on freeways may change accordingly, and new collective phenomena, which are only marginally known at present, could emerge. Yet, as ACC systems are introduced as comfort options and their operation is entirely under the responsibility of the driver, vehicle manufacturers do not have explicit requirements to fulfill nor they have to provide any evidence about their performances. As a result, any safety implication connected to their interactions with other road users escapes any monitoring and opportunity of improvement. This work presents a set of experimental car-following campaigns, providing an overview of the behavior of commercial ACC systems under different driving conditions. Furthermore, the suggestion of a unified data structure across the different tests facilitates comparison between the different campaigns, vehicles, systems and specifications. The complete data is published as an open-access database (OpenACC), available to the research community. As more test campaigns will be carried out, OpenACC will evolve accordingly. The activity is performed in the framework of the openData policy of the European Commission Joint Research Centre with the objective to engage the whole scientific community towards a better understanding of the properties of ACC vehicles in view of anticipating their possible impacts on traffic flow and prevent possible problems connected to their widespread introduction. In this light, OpenACC, over time, also aims at becoming a reference point to study if and how the parameters of such systems need to be regulated, how homogeneously they behave, how new ACC car-following models should be designed for traffic microsimulation purposes and what are the key differences between ACC systems and human drivers. [ABSTRACT FROM AUTHOR]