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Connected and automated vehicles (CAVs) promise to significantly improve road traffic. To a certain extent, this situation is similar to the expectations at the end of the last century about the positive effects that the introduction of Adaptive Cruise Control (ACC) systems would have had on motorway traffic. The parallelism is interesting because ACC equipped vehicles represent the first level of vehicle automation and are now widely available on the market. In this light, studying ACC impacts can help to anticipate potential problems related to its widespread application and to avoid that AVs and CAVs will lead to the same problems. Only a few test-campaigns had been carried out studying the ACC impacts under real-world driving conditions in quantitative terms. To bridge this gap, the Joint Research Centre of the European Commission has organized a number of experimental campaigns involving several ACC-equipped vehicles to study different implications of their widespread. In this context, the present paper summarizes the outcomes of a test campaign involving 10 commercially available ACC-equipped vehicles. The test campaign has been executed in two different test-tracks of the ZalaZONE proving ground, in Hungary. The tests have been carried out at low-speeds (30–60 km/h) and have involved platoons of vehicles of different brands and different powertrains, which were tested in a variety of vehicle orders and with different settings of their ACC systems. Test results have been used to derive information about the properties of the different ACC systems, to study their string stability, to study the effect of ACC systems on traffic flow, and to draw inference about the possible implications on energy consumption and traffic safety. Results confirm the previous findings in terms of string instability of the ACC and highlight that in the present form, ACC systems will possibly lead to higher energy consumption and introduce new safety risks when their penetration in the fleet increases. However, they also highlight that the materialization of the above findings for AVs depends on the operational logic that manufacturers will adopt during the implementation phase. Therefore, results suggest that functional requirements to guarantee string stability and in general to not disrupt the normal flow of traffic should be introduced both for ACC and for any automated system that will be placed on the market in the future. [ABSTRACT FROM AUTHOR]

Background: Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods: Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results: Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%). Conclusions: In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off. [ABSTRACT FROM AUTHOR]

Background: Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods: Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results: Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7 – 20.5 % higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2 – 17.3 %). Conclusions: In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off. [ABSTRACT FROM AUTHOR]

• 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]

• Two Fuzzy Surrogate Safety Metrics (FSSMs) for rear-end collision are presented. • Unsafe situations are distinguished between critically and proactively unsafe. • The new metrics are assessed based on test track experiments. • FSSMs outperform traditional metrics based on crisps thresholds. • FSSMs are suitable for supporting driving assistance and automation systems. The present paper discusses two fuzzy Surrogate Safety Metrics (SSMs) for rear-end collision, the Proactive Fuzzy SSM (PFS) and Critical Fuzzy SSM (CFS). The objective is to investigate their applicability for evaluating the real-time rear-end risk of collision of vehicles to support the operations of advanced driver assistance and automated vehicle functionalities (from driving assistance systems to fully automated vehicles). The proposed Fuzzy SSMs are evaluated and compared to other traditional metrics on the basis of empirical observations. To achieve this goal, an experimental campaign was organized in the AstaZero proving ground in Sweden. The campaign consisted of two main parts: a car-following experiment with five vehicles solely driven by Adaptive Cruise Control (ACC) systems and a safety critical experiment, testing the response of the Autonomous Emergency Braking (AEB) system to avoid collisions on a static target. The proposed PFS is compared with the safe distance defined by the well-known Responsibility Sensitive Safety (RSS) model, showing that it can produce meaningful results in assessing safety conditions also without the use of crisp safety thresholds (like in the case of RSS). The CFS outperformed the well-known Time-To-Collision (TTC) SSM in the a-priori identification of the cases, where the tested vehicles were not able to avoid the collision with the static target. Moreover, results show that CFS at the time of the first deceleration is correlated with the velocity of the vehicle at the time of collisions with the target. [ABSTRACT FROM AUTHOR]