Benchmarking the driver acceleration impact on vehicle energy consumption and CO2 emissions.

The study proposes a methodology for quantifying the impact of real-world heterogeneous driving behavior on vehicle energy consumption, linking instantaneous acceleration heterogeneity and CO2 emissions. Data recorded from 20 different drivers under real driving are benchmarked against the Worldwide Harmonized Light Vehicle Test Cycle (WLTC), first by correlating the speed cycle with individual driver behavior and then by quantifying the CO2 emissions and consumption. The vehicle-Independent Driving Style metric (IDS) is used to quantify acceleration dynamicity, introducing driving style stochasticity by means of probability distribution functions. Results show that the WLTC cycle assumes a relatively smooth acceleration style compared to the observed ones. The method successfully associates acceleration dynamicity to CO2 emissions. We observe a 5% difference in the CO2 emissions between the most favourable and the least favourable case. The intra-driver variance reached 3%, while the inter-driver variance is below 2%. The approach can be used for quantifying the driving style induced emissions divergence.

An acceptance divergence? Media, citizens and policy perspectives on autonomous cars in the European Union.

Do citizens, media and policymakers share the same view on autonomous cars? In the present paper, we analyse data from media articles, a Eurobarometer survey, and policy documents, to understand the perspective of different stakeholders when it comes to autonomous cars. We find significant differences between the groups, with a predominance of negative sentiments in news articles and a majority of citizens being wary of autonomous cars, while the political narrative mostly carries a positive tone. The findings highlight a dichotomous perspective about this potentially disruptive technology. This may represent a problem as the benefits of adopting autonomous cars will only come to surface if all actors are engaged and see the advantages they can bring to people's daily lives. We conclude by encouraging policymakers to promote initiatives to engage citizens in the transformation of road transport and other stakeholders to be advertised the positive implications of autonomous vehicles.

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.

Gaseous Emissions from Light-Duty Vehicles: Moving from NEDC to the New WLTP Test Procedure.

The Worldwide Harmonized Light Duty Test Procedure (WLTP), recently issued as GTR15 by UNECE-WP29, is designed to check the pollutant emission compliance of Light Duty Vehicles (LDVs) around the world and to establish the reference vehicle fuel consumption and CO2 performance. In the course of the development of WLTP, the Joint Research Center (JRC) of the European Commission has tested gaseous emissions of twenty-one Euro 4-6 gasoline and diesel vehicles, on both the current European type approval test procedure (NEDC) and the progressive versions of the WLTP. The results, which should be regarded just as an initial and qualitative indication of the trends, demonstrated minimal average differences between CO2 emissions over the NEDC and WLTP. On the other hand, CO2 emissions measured at JRC on the NEDC were on average 9% higher than the respective type approval values, therefore suggesting that for the tested vehicles, CO2 emissions over WLTP were almost 10% higher than the respective NEDC type approval values. That difference is likely to increase with application of the full WLTP test procedure. Measured THC emissions from most vehicles stayed below the legal emission limits and in general were lower under the WLTP compared to NEDC. Moving from NEDC to WLTP did not have much impact on NOx from gasoline vehicles and CO from diesel vehicles. On the contrary, NOx from diesel vehicles and CO from low-powered gasoline vehicles were significantly higher over the more dynamic WLTP and in several cases exceeded the emission limits. Results from this study can be considered indicative of emission patterns of modern technology vehicles and useful to both policy makers and vehicle manufacturers in developing future emission policy/technology strategies.

Climate-based archetypes for the environmental fate assessment of chemicals.

Emissions of chemicals have been on the rise for years, and their impacts are greatly influenced by spatial differentiation. Chemicals are usually emitted locally but their impact can be felt both locally and globally, due to their chemical properties and persistence. The variability of environmental parameters in the emission compartment may affect the chemicals' fate and the exposure at different orders of magnitude. The assessment of the environmental fate of chemicals and the inherent spatial differentiation requires the use of multimedia models at various levels of complexity (from a simple box model to complex computational and high-spatial-resolution models). The objective of these models is to support ecological and human health risk assessment, by reducing the uncertainty of chemical impact assessments. The parameterisation of spatially resolved multimedia models is usually based on scenarios of evaluative environments, or on geographical resolutions related to administrative boundaries (e.g. countries/continents) or landscape areas (e.g. watersheds, eco-regions). The choice of the most appropriate scale and scenario is important from a management perspective, as a balance should be reached between a simplified approach and computationally intensive multimedia models. In this paper, which aims to go beyond the more traditional approach based on scale/resolution (cell, country, and basin), we propose and assess climate-based archetypes for the impact assessment of chemicals released in air. We define the archetypes based on the main drivers of spatial variability, which we systematically identify by adopting global sensitivity analysis techniques. A case study that uses the high resolution multimedia model MAPPE (Multimedia Assessment of Pollutant Pathways in the Environment) is presented. Results of the analysis showed that suitable archetypes should be both climate- and chemical-specific, as different chemicals (or groups of them) have different traits that influence their spatial variability. This hypothesis was tested by comparing the variability of the output of MAPPE for four different climatic zones on four different continents for four different chemicals (which represent different combinations of physical and chemical properties). Results showed the high suitability of climate-based archetypes in assessing the impacts of chemicals released in air. However, further research work is still necessary to test these findings.

Regional mobility during the Covid-19 pandemic: Analysis of trends and repercussions using mobile phones data across the EU.

The analysis of mobile phones data at regional level in the EU reveals varying patterns in mobility trends during the Covid-19 pandemic. These depend on the temporal evolution of the pandemic in each EU Member State, the measures taken at local or national level to limit the growth of the pandemic, as well as the level of urbanization and type of economic activity in each region. During the first phase of the pandemic (March- April 2020) the decrease in mobility was in general uniform among regions in the same Member State, especially in Italy, Spain and France, where national level measures were adopted. A relaxation of the measures and a resulting rebound of mobility was evident during the summer period (July- August 2020). At the same time, a shift from urban to rural areas during the summer vacation period is evident, with especially touristic areas increasing the number of movements in the same Member State. The variance in mobility trends during the second wave of the pandemic (October- November 2020) was higher, a result of the predominantly local and regional level measures applied in each Member State. Those insights suggest a certain correlation between the level of mobility and the evolution of the pandemic at regional level. The association with high levels of Covid-19 prevalence is particularly strong in urban regions with high mobility levels.

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.

How will vehicle automation and electrification affect the automotive maintenance, repair sector?

Automation and electrification in road transport are trends that will influence several economic sectors of the European economy. The automotive maintenance and repair (M&R) sector will experience the effects of such transitions in the long term. This paper assesses the research in the road transport to derive the factors that may influence the M&R demand based on Battery Electric Vehicles (BEVs) and Autonomous Vehicles (AVs) uptake. Starting from current scientific research and grounded on interviews with experts, the paper reviews major drivers influencing M&R demand and provides indications on possible future effects. While for BEVs, previous work has been conducted to estimate the M&R cost variations, the research addressing the impacts of AVs deployment on the M&R sector is at its incipient stage, hence the views of experts were paramount to shed light on this topic. We identified a scientific consensus that BEVs have less M&R requirements compared with Conventional Vehicles (CVs). For AVs, our analysis and expert views identify some important factors influencing M&R requirements: hardware components, software that enables autonomy, the rise in vehicle kilometres travelled leading to higher wear and tear of replaceable parts, the need for adequate cleaning services, especially for fleets and shared vehicles. Further work should look at the impact of regulations and the non-insurable risks linked to M&R requirements.

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.

Effect of Low Ambient Temperature on Emissions and Electric Range of Plug-In Hybrid Electric Vehicles.

Plug-in hybrid electrical vehicles (PHEVs) are generally considered to be a cleaner alternative to conventional passenger cars. However, there is still very limited information available regarding criteria pollutant emissions from these vehicles. This paper shows, for the first time, the emissions of criteria pollutants, unregulated pollutants, and CO2 and also electric range from two very different PHEVs, one Euro 6 parallel plug-in hybrid and one range-extended battery electric vehicle (BEVx), applying the new world harmonized light-duty test procedure at ambient temperatures equal to 23 and -7 °C. The impact of using a cabin air heating system on vehicle electric range and emissions at cold temperature has also been studied. Cold ambient temperatures and, to a larger extent, the use of heating systems have been shown to lead to a pronounced negative impact on emissions and shorter electric ranges. Results also show that modern PHEVs can emit similar, or even higher, levels of pollutants (e.g., particle number) as Euro 6 conventional gasoline and diesel vehicles.