This paper investigates the influence of tire-road friction on the performance of a long combination vehicle, specifically the A-double, during low-speed manoeuvres such as navigating roundabouts and intersections. Using real-world naturalistic driving data and applying the Performance-Based Standards scheme, the study assesses the low-speed swept path (LSSP) and the driver’s response to varying friction conditions. Results indicate that tire-road friction has minimal effect on the LSSP, which conforms with earlier simulation-based studies. However, it influences driver behaviour, particularly in roundabouts where speed reductions are observed under low-friction conditions. 

The deployment of High Capacity Transport (HCT) vehicles is in process in different countries. Although their performance has been assessed through simulations and test-track experiments, a question that remains unanswered is: how do these vehicles perform in real traffic? In this paper, the question is addressed for one of the transient manoeuvres, i.e., a lane change using Naturalistic Driving Data (NDD). First, an algorithm is proposed to extract lane changes from the NDD of HCT vehicles using GPS, road data and IMU signals. Following this, the performance of two A-double combinations is assessed in the extracted lane changes using measures commonly used in performance-based standards (PBS) schemes like offtracking and rearward amplification. The dependency of these measures on the factors such as the vehicle’s speed, load and lateral displacement is investigated. The assessment concludes that the vehicles satisfy the PBS requirements proposed for them and are driven safely in the extracted lane changes.

The focus of this paper is to use Naturalistic Driving Data to understand how the drivers manoeuvre an A-double combination in the roundabouts and evaluate performance in the roundabouts using measures like Low-Speed Swept Path (LSSP) and Tail Swing (TS). The analyses of the steering patterns and speed variations depict that the standard deviations of the responses of the drivers for a given travel direction in a roundabout are within 35o (17 % of the baseline) for the steering wheel angle and 8 km/h (40 % of the baseline) for the speed. It is also found that the cognitive workload of the drivers due to the steering pattern is higher in right turns compared to straight crossings through the roundabout. The performance analyses show a dependency of LSSP on the instantaneous radius obtained from the vehicle's path, and the vehicle's travel direction in the roundabout. LSSP ranges from 7.7 m for a left turn in a roundabout with an inner radius of 12 m to 3.1 m for a straight crossing in a roundabout with a 30 m inner radius. TS is observed in only one roundabout and its magnitude goes up to 0.4 m in a roundabout of 30 m inner radius.

A methodology for the generation of representative driving cycles is proposed and evaluated. The proposed method combines traffic simulation and driving behavior modeling to generate mission-based driving cycles. Extensions to the existing behavioral model in a traffic simulation tool are suggested and parameterized for different driver categories to capture the effects of road geometry and variances between drivers. The evaluation results illustrate that the developed extensions significantly improve the match between driving data and the driving cycles generated by traffic simulation. Using model extensions parameterized for different driver categories, instead of only one average driver, provides the possibility to represent different driving behaviors and further improve the realism of the resulting driving cycles.

The automotive industry is facing a major challenge to reduce environmental impacts. As a consequence, the increasing diversity of powertrain configurations put a demand on testing and evaluation procedures. One of the key tools for this purpose is simulators. In this paper a powertrain model and a procedure for parameterizing it, using chassis dynamometers and a developed pedal robot are presented. The parameterizing procedure uses the on-board diagnostics of the car and does not require any additional invasive sensors.

Thus, the developed powertrain model and parameterization procedure provide a rapid non- invasive way of modelling powertrains of test cars. The parameterizing procedure has been used to model a front wheel drive Golf V with a 1.4L multi-fuel engine and a manual gearbox. The achieved results show a good match between simulation results and test data. The powertrain model has also been tested in real-time in a driving simulator.

To better utilize the potential of system simulation models and simulators, industrially applicable methods for Verification, Validation and Uncertainty Quantification(VV&UQ) are crucial. This paper presents an exploratory case study of VV&UQ techniquesapplied on models integrated in aircraft system simulators at Saab Aeronauticsand in driving simulators at the Swedish National Road and Transport Research Institute(VTI). Results show that a large number of Verification and Validation (V&V)techniques are applied, some of which are promising for further development and use insimulator credibility assessment. Regarding the application of UQ, a large gap betweenacademia and this part of industry has been identified, and simplified methods areneeded. The applicability of the NASA Credibility Assessment Scale (CAS) at the studied organizations is also evaluated and it can be concluded that the CAS is consideredto be a usable tool for achieving a uniform level of V&V for all models included in asimulator, although its implementation at the studied organizations requires tailoringand coordination.

Time-to-collision (TTC) is a widely used measure for predicting rear-end collisions, assuming constant speed and heading for both vehicles in the prediction horizon. However, this conventional formulation cannot detect sideswipe collisions. A two-dimensional extension, TTC_2D, has been proposed in the literature to address lateral interactions. However, this formulation assumes both vehicles have the same heading and that their headings remain unchanged during the manoeuvre, in addition to the constant speed and heading assumptions in the prediction horizon. Moreover, its use for articulated vehicles like a tractor-semitrailer remains unclear. This paper proposes three enhanced versions of TTC_2D to overcome these limitations. The first incorporates the vehicle heading to account for directional differences. The standard assumption of constant speed and heading in the prediction horizon holds. The second adapts the formulation for articulated vehicles, and the third allows for constant acceleration, relaxing the constant speed assumption in the prediction horizon. All versions are evaluated in simulated cut-in scenarios, covering both sideswipe and rear-end collisions, using the CARLA simulation environment with a tractor-semitrailer model. Results show that the proposed versions predict sideswipe collisions with better accuracy compared to existing TTC2_D. They also detect rear-end collisions similar to the existing methods.