Trip Based Modeling of Fuel Consumption in Modern Heavy-Duty Vehicles Using Artificial Intelligence

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle speed (km/h). Instantaneous fuel consumption data can help to predict patterns in fuel consumption for optimized fleet operations. In this work, the data used for modeling was collected at a frequency of 1Hz during on-road testing of modern heavy-duty vehicles (HDV) at the West Virginia University Center for Alternative Fuels Engines and Emissions (WVU CAFEE) using the portable emissions monitoring system (PEMS). The performance of the artificial neural network was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The model was further evaluated with data collected from a vehicle on-road trip. The study shows that artificial neural networks performed slightly better than other machine learning techniques such as linear regression (LR), and random forest (RF), with high R-squared (R2) and lower root mean square error.

Impact of post-stroke cognitive dysfunction on driving ability

The paper aims to present the current views on the impact of post-stroke cognitive deficits on driving ability, and diagnostic assessment practices in this area. Firstly, the neuropsychological consequences of stroke are briefly presented. This part focuses specifically on visuospatial and executive dysfunctions. Among those dysfunctions, unilateral neglect, especially as it is frequently associated with anosognosia, may have the greatest impact on driving ability, leading to an increased accident risk. Then, different approaches to assessing fitness to drive after stroke are presented, including on-road testing, testing with the use of simulator, and clinical assessment focusing on cognition. The role of cognitive assessment in predicting fitness to drive is described in more detail. The Clock Drawing Test is the most appropriate screening measure in this context, as it engages both visuospatial and executive functions. The Trail Making Test is the most popular working memory test in the context of drivers’ assessment, as it requires visual search and psychomotor speed. The Rey Complex Figure Test is another commonly used test. It requires visuospatial and executive functions, and may also serve as a measure of visuospatial memory. Finally, the legal aspects of the assessments are discussed with reference to the practices used in Great Britain, Belgium, Sweden, and Germany. In Poland, there are no detailed standards for post-stroke fitness-to-drive assessment.

NH3 and N2O Real World Emissions Measurement from a CNG Heavy Duty Vehicle Using On-Board Measurement Systems

The development and utilization of a series of after-treatment devices in modern vehicles has led to an increase in emissions of NH3 and/or N2O with respect to the past. N2O is a long-lived greenhouse gas and an ozone-depleting substance, while NH3 is a precursor of secondary aerosols in the atmosphere. Certain regions, e.g., the EU and the USA, have introduced limits to the emissions of NH3 or N2O for vehicles tested in the laboratory. However, due to the lack of on-board systems that allow for the measurement of these compounds when the regulations were developed, these vehicles’ real-world emissions have not been regulated. This work evaluates on-board systems that could allow measuring real-world emissions of NH3 and N2O from heavy-duty vehicles. In particular, emissions of NH3 or N2O from a Euro VI Step D urban/interurban bus fueled with Compressed Natural Gas were measured using the HORIBA’s OBS-ONE-XL, which is based on a specifically developed technique called Infrared Laser Absorption Modulation, and uses a Quantum Cascade Laser as a light source. They were also measured using the PEMS-LAB, which is a more conventional FTIR-based system. Emissions were measured under real-world driving conditions on the road and in a climatic test cell at different ambient temperatures. For most of the conditions tested, the on-board systems correlated well with a laboratory-grade FTIR used as reference. In addition, a good correlation with R2 > 0.9 was found for the N2O concentrations measured by OBS-ONE-XL and PEMS-LAB during on-road testing.

Design and development of MR damper for two wheeler application and Kwok model parameters tuning for designed damper

Magnetorheological dampers have been the interest of many researchers for a few decades for the reason of being an effective and rapidly progressing technology in the field of semi-active controlled suspension. The dynamic behaviour of these devices with nonlinear hysteresis is quite a complicated phenomenon. Hence, this paper aims at the design, modelling and simulation of a custom-made MR damper for a two-wheeler vehicle. The Kwok model has been chosen to mathematically model the MR damper. The model parameters have been optimised by minimizing the error difference between experimental and model-generated force results. A PID control is designed to control the damper effectively depending on the deflection of the damper. The two-wheeler vehicle modelled with four degrees of freedom is coupled with a mathematical model of MR damper in front and rear suspension. Further, the dynamic analysis has been performed in MATLAB/Simulink considering random road input for different velocities and current input conditions. The improved performance of MR damper was observed in suppressing road irregularities using a PID controller. As an implementation part of the work, the developed damper has been implemented in a two wheeler vehicle for performance evaluation at on-road testing conditions. The results showed significant improvement in damper performance with increment of constant current controlling MR dampers.

A Systematic Framework to Identify Violations of Scenario-dependent Driving Rules in Autonomous Vehicle Software

Safety compliance is paramount to the safe deployment of autonomous vehicle (AV) technologies in real-world transportation systems. As AVs will share road infrastructures with human drivers and pedestrians, it is an important requirement for AVs to obey standard driving rules. Existing AV software testing methods, including simulation and road testing, only check fundamental safety rules such as collision avoidance and safety distance. Scenario-dependent driving rules, including crosswalk and intersection rules, are more complicated because the expected driving behavior heavily depends on the surrounding circumstances. However, a testing framework is missing for checking scenario-dependent driving rules on various AV software. In this paper, we design and implement a systematic framework AVChecker for identifying violations of scenario-dependent driving rules in AV software using formal methods. AVChecker represents both the code logic of AV software and driving rules in proposed formal specifications and leverages satisfiability modulo theory (SMT) solvers to identify driving rule violations. To improve the automation of systematic rule-based checking, AVChecker provides a powerful user interface for writing driving rule specifications and applies static code analysis to extract rule-related code logic from the AV software codebase. Evaluations on two open-source AV software platforms, Baidu Apollo and Autoware, uncover 19 true violations out of 28 real-world driving rules covering crosswalks, traffic lights, stop signs, and intersections. Seven of the violations can lead to severe risks of a collision with pedestrians or blocking traffic.

Application of Combining YOLO Models and 3D GPR Images in Road Detection and Maintenance

Improving the detection efficiency and maintenance benefits is one of the greatest challenges in road testing and maintenance. To address this problem, this paper presents a method for combining the you only look once (YOLO) series with 3D ground-penetrating radar (GPR) images to recognize the internal defects in asphalt pavement and compares the effectiveness of traditional detection and GPR detection by evaluating the maintenance benefits. First, traditional detection is conducted to survey and summarize the surface conditions of tested roads, which are missing the internal information. Therefore, GPR detection is implemented to acquire the images of concealed defects. Then, the YOLOv5 model with the most even performance of the six selected models is applied to achieve the rapid identification of road defects. Finally, the benefits evaluation of maintenance programs based on these two detection methods is conducted from economic and environmental perspectives. The results demonstrate that the economic scores are improved and the maintenance cost is reduced by $49,398/km based on GPR detection; the energy consumption and carbon emissions are reduced by 792,106 MJ/km (16.94%) and 56,289 kg/km (16.91%), respectively, all of which indicates the effectiveness of 3D GPR in pavement detection and maintenance.

Risk Mitigation Planning for Revenue Service Testing of Bus Automated Emergency Braking

In 2017, the Federal Transit Administration awarded Pierce Transit of Lakewood, WA, a $1.66 m grant for a bus collision avoidance and mitigation safety research and demonstration project. The project scope includes installation of an advanced technology package, the Pedestrian Avoidance Safety System (PASS) that uses light detection and ranging (LiDAR) sensors to trigger automated deceleration and braking. Thirty transit buses are being equipped with PASS and will be monitored using telematics to transmit and collect critical test data. The test plan includes collecting data while operating the buses in “stealth mode” with PASS detecting and logging events, but not activating brakes automatically or warning the drivers. At the conclusion of “stealth mode” operation, Pierce Transit will make a go/no-go decision on whether to activate PASS’s automatic deceleration and braking functionality for revenue service with passengers. This paper describes the risk mitigation process developed to determine if the system is safe enough to allow operation in revenue service. The process includes: broad stakeholder engagement, constituting an ad-hoc working group within Pierce Transit to advise executive management, development of decision-making criteria, consultation with state and federal officials on regulatory requirements and compliance, review of applicable standards and engineering test protocols, engineering consultations with the bus original equipment manufacturer, and road testing to simulate revenue service, collect data, and obtain feedback from drivers and maintainers.