Multianticipation for string stable Adaptive Cruise Control and increased motorway capacity without vehicle-to-vehicle communication.

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.

Driver models for the definition of safety requirements of automated vehicles in international regulations. Application to motorway driving conditions.

UN Regulation 157, the first global regulation regarding the type-approval of Automated Driving Systems (ADS), has been adopted in 2021. In it, safety performance requirements are being defined for vehicles of automation Level 3, according to the SAE J3016, with a limited Operational Design Domain (ODD). In particular, for three types of events that are related to motorway driving, two models are provided to distinguish between preventable traffic scenarios, for which the ADS is expected to avoid an accident, and unpreventable traffic scenarios, for which accidents cannot be avoided and the ADS can only mitigate their severity. The models recreate the short-term behavior of a driver who reacts to an emergency. Two possible actions are predicted: either no reaction or full braking when danger is identified. In the present paper the two models are analyzed and compared with two additional models: an industry proposed model, the Responsibility Sensitive Safety framework (RSS), and the Fuzzy Safety Model (FSM) proposed by the authors. As in the case of the two regulation models, also the RSS, although more sophisticated, assumes that the possible reaction by the driver is binary. This approach neglects the ability of a human driver to drive defensively and anticipate possible risks. Defensive drivers, indeed, may use comfortable decelerations in anticipation, to avoid finding themselves in an emergency situation. The FSM uses fuzzy logic to mimic this behavior. Results show that anticipation plays a very important role to reduce the number of unpreventable traffic scenarios. In addition, by validating the classification capabilities of the four models with real traffic data, the FSM proved to be the most suitable of the investigated models. On the basis of these results, the FSM has been included in the proposal for amending UN Regulation 157, thus allowing to set higher safety standards for the first automated vehicles that will be introduced into the market.

Fuzzy Surrogate Safety Metrics for real-time assessment of rear-end collision risk. A study based on empirical observations.

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.