A Methodology for Evaluating Driving Styles in Various Road Conditions

How drivers perform under different scenarios: Ability-related driving style extraction for large-scale dataset

The extraction and analysis of driving style are essential for a comprehensive understanding of human driving behaviours. Most existing studies rely on subjective questionnaires and specific experiments, posing challenges in accurately capturing authentic characteristics of group drivers in naturalistic driving scenarios. As scenario-oriented naturalistic driving data collected by advanced sensors becomes increasingly available, the application of data-driven methods allows for a exhaustive analysis of driving styles across multiple drivers. Following a theoretical differentiation of driving ability, driving performance, and driving style with essential clarifications, this paper proposes a quantitative determination method grounded in large-scale naturalistic driving data. Initially, this paper defines and derives driving ability and driving performance through trajectory optimisation modelling considering various cost indicators. Subsequently, this paper proposes an objective driving style extraction method grounded in the Gaussian mixture model. In the experimental phase, this study employs the proposed framework to extract both driving abilities and performances from the Waymo motion dataset, subsequently determining driving styles. This determination is accomplished through the establishment of quantifiable statistical distributions designed to mirror data characteristics. Furthermore, the paper investigates the distinctions between driving styles in different scenarios, utilising the Jensen-Shannon divergence and the Wilcoxon rank-sum test. The empirical findings substantiate correlations between driving styles and specific scenarios, encompassing both congestion and non-congestion as well as intersection and non-intersection scenarios.

Experimental Study on Longitudinal Acceleration of Urban Buses and Coaches in Different Road Maneuvers

A vehicle's longitudinal acceleration is a parameter often used for determining vehicle motion dynamics. This parameter can also be used to evaluate driver behavior and passenger comfort analysis. The paper presents the results of longitudinal acceleration tests of city buses and coaches recorded during rapid acceleration and braking maneuvers. The presented test results demonstrate that longitudinal acceleration is significantly affected by road conditions and surface type. In addition, the paper presents the values of longitudinal accelerations of city buses and coaches during their regular operation. These results were obtained on the basis of registration of vehicle traffic parameters in a continuous and long-term manner. The test results showed that the maximum deceleration values recorded during the tests of city buses and coaches in real traffic conditions were much lower than the maximum deceleration values found during sudden braking maneuvers. This proves that the tested drivers in real conditions did not have to use sudden braking. The maximum positive acceleration values recorded in acceleration maneuvers were slightly higher than the acceleration values logged during the rapid acceleration tests on the track.

Analysis of the Impact of Invisible Road Icing on Selected Parameters of a Minibus Vehicle

The measurement of acceleration during vehicle motion can be used to assess the driving styles and behaviours of drivers, to control vehicle traffic, to detect uncontrolled vehicle behaviour, and to prevent accidents. The measurement of acceleration during vehicle motion on an icy road can be used to warn the driver about changing conditions and the related hazards. This paper presents the results of testing the motion parameters of a Ford Transit adapted for passenger transport in critical traffic conditions. It can contribute to the improvement of road safety. Critical traffic conditions are deemed in the paper as sudden braking, rapid acceleration, and circular vehicle motion at maximum speed maintainable in the given conditions. The vehicle's acceleration and speed were measured during the tests. The tests were carried out with a TAA linear acceleration sensor and a Correvit S-350 Aqua optoelectronic sensor. The same test runs were conducted on a dry surface, a wet (after rain) surface and a surface covered with a thin, invisible ice layer. The objective of the tests was to determine the impact of invisible road icing, the so-called black ice, on the tested vehicle's braking, acceleration, and circular motion. It was demonstrated that a virtually invisible ice layer covering the road surface has a substantial impact on the tested vehicle's motion parameters, thereby affecting traffic safety. It substantially extends the braking and acceleration distances and requires the driver to reduce the vehicle's speed when performing circular motions. A clear wet surface, representing motion after rain, did not substantially affect the analysed parameters. The obtained results can be used in traffic simulations and to analyse the causes of accidents.

Research Scenarios of Autonomous Vehicles, the Sensors and Measurement Systems Used in Experiments

Automated and autonomous vehicles are in an intensive development phase. It is a phase that requires a lot of modelling and experimental research. Experimental research into these vehicles is in its initial state. There is a lack of findings and standardized recommendations for the organization and creation of research scenarios. There are also many difficulties in creating research scenarios. The main difficulties are the large number of systems for simultaneous checking. Additionally, the vehicles have a very complicated structure. A review of current publications allowed for systematization of the research scenarios of vehicles and their components as well as the measurement systems used. These include perception systems, automated response to threats, and critical situations in the area of road safety. The scenarios analyzed ensure that the planned research tasks can be carried out, including the investigation of systems that enable autonomous driving. This study uses passenger cars equipped with highly sophisticated sensor systems and localization devices. Perception systems are the necessary equipment during the conducted study. They provide recognition of the environment, mainly through vision sensors (cameras) and lidars. The research tasks include autonomous driving along a detected road lane on a curvilinear track. The effective maintenance of the vehicle in this lane is assessed. The location used in the study is a set of specialized research tracks on which stationary or moving obstacles are often placed.

Vehicle and Traffic Safety

The role of the road transportation of people and goods is increasing [...]

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.

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.

Determination of Turning Radius and Lateral Acceleration of Vehicle by GNSS/INS Sensor

In this article, we address the determination of turning radius and lateral acceleration acting on a vehicle up to 3.5 t gross vehicle mass (GVM) and cargo in curves based on turning radius and speed. Global Navigation Satellite System with Inertial Navigation System (GNSS/INS) dual-antenna sensor is used to measure acceleration, speed, and vehicle position to determine the turning radius and determine the proper formula to calculate long average lateral acceleration acting on vehicle and cargo. The two methods for automatic selection of events were applied based on stable lateral acceleration value and on mean square error (MSE) of turning radiuses. The models of calculation of turning radius are valid for turning radius within 5-70 m for both methods of automatic selection of events with mean root mean square error (RMSE) 1.88 m and 1.32 m. The models of calculation of lateral acceleration are valid with mean RMSE of 0.022 g and 0.016 g for both methods of automatic selection of events. The results of the paper may be applied in the planning and implementation of packing and cargo securing procedures to calculate average lateral acceleration acting on vehicle and cargo based on turning radius and speed for vehicles up to 3.5 t GVM. The results can potentially be applied for the deployment of autonomous vehicles in solutions grouped under the term of Logistics 4.0.

Assessing the Environmental Performances of Urban Roundabouts Using the VSP Methodology and AIMSUN

In line with globally shared environmental sustainability goals, the shift towards citizen-friendly mobility is changing the way people move through cities and road user behaviour. Building a sustainable road transport requires design knowledge to develop increasingly green road infrastructures and monitoring the environmental impacts from mobile crowdsourced data. In this view, the paper presents an empirically based methodology that integrates the vehicle-specific power (VSP) model and microscopic traffic simulation (AIMSUN) to estimate second-by-second vehicle emissions at urban roundabouts. The distributions of time spent in each VSP mode from instantaneous vehicle trajectory data gathered in the field via smartphone were the starting point of the analysis. The versatility of AIMSUN in calibrating the model parameters to better reflect the field-observed speed-time trajectories and to enhance the estimation accuracy was assessed. The conversion of an existing roundabout within the sample into a turbo counterpart was also made as an attempt to confirm the reproducibility of the proposed procedure. The results shed light on new opportunities in the environmental performance evaluation of road units when changes in design or operation should be considered within traffic management strategies and highlighted the potential of the smart approach in collecting big amounts of data through digital communities.

Parameters Influencing on Electric Vehicle Range

There is a range of anxiety-related phenomena among users and potential buyers of electric vehicles. Chief among them is the fear of the vehicle stopping and its users getting “stuck” before reaching their designated destination. The limited range of an electric vehicle makes EV users worry that the battery will drain while driving and the vehicle will stall on the road. It is therefore important to know the factors that could further reduce the range during daily vehicle operation. The purpose of this study was to determine the effect of selected parameters on a battery’s depth of discharge (DOD). In a simulation study of an electric vehicle, the effects of the driving cycle, ambient temperature, load, and initial state of charge of the accumulator on the energy consumption pattern and a battery’s depth of discharge (DOD) were analyzed. The simulation results confirmed that the route taken has the highest impact on energy consumption. The presented results show how significantly the operating conditions of an electric vehicle affect the energy life. This translates into an electric vehicle’s range.