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The Particle Number–Portable Emission Measurement System (PN-PEMS) came into force with Euro VI Phase E regulations starting January 1, 2022. However, positive ignition (PI) engines must comply from January 1, 2024. The delay was due to the unavailability of the PN-PEMS system that could withstand high concentrations of water typically present in the tailpipe (TP) of CNG vehicles, which was detrimental to the PN-PEMS systems. Thus, this study was designed to evaluate the condensation particle counter (CPC)-based PN-PEMS measurement capabilities that was upgraded to endure high concentration of water. The PN-PEMS measurement of solid particle number (SPN23) greater than 23 nm was compared against the laboratory-grade PN systems in four phases. Each phase differs based upon the PN-PEMS and PN system location and measurements were made from three different CNG engines. In the first phase, systems measured the diluted exhaust through constant volume sampler (CVS) tunnel.

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions.

Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors.

Evaluating real-world hazards associated with perception subsystems is critical in enhancing the performance of autonomous vehicles. The reliability of autonomous vehicles perception subsystems are paramount for safe and efficient operation. While current studies employ different metrics to evaluate perception subsystem failures in autonomous vehicles, there still exists a gap in the development and emphasis on engineering requirements. To address this gap, this study proposes the establishment of engineering requirements that specifically target real-world hazards and resilience factors important to AV operation, using High-Definition Maps, Global Navigation Satellite System, and weather sensors. The findings include the need for engineering requirements to establish clear criteria for a high-definition maps functionality in the presence of erroneous perception subsystem inputs which enhances the overall safety and reliability of the autonomous vehicles.

Estimated engine torque is an important parameter used by automotive systems for automated transmission and clutch control. Heavy-duty engine and transmission manufacturers widely use SAE J -1939 based ECU torque calculation based on mass air/fuel flow steady state maps created during calibration of the engine for this purpose. As an alternative, to enhance the accuracy of this important control variable, a virtual flywheel torque sensor (VFTS) was developed. It measures the engine torque based on the harmonics of the instantaneous flywheel speed signal. Initial dynamometer testing showed the VFTS estimated torque values exhibited a maximum inaccuracy of 12% of the actual measured torque over the range of conditions tested. In this paper we report the results of on road truck testing of the VFTS. A loaded heavy truck with a gross vehicle weight rating of 80,000 pounds was used.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

Contemporary ADS and ADAS localization technology utilizes real-time perception sensors such as visible light cameras, radar sensors, and lidar sensors, greatly improving transportation safety in sufficiently clear environmental conditions. However, when lane lines are completely occluded, the reliability of on-board automated perception systems breaks down, and vehicle control must be returned to the human driver. This limits the operational design domain of automated vehicles significantly, as occlusion can be caused by shadows, leaves, or snow, which all occur in many regions. High-definition map data, which contains a high level of detail about road features, is an alternative source of the required lane line information. This study details a novel method where high-definition map data are processed to locate fully occluded lane lines, allowing for automated path planning in scenarios where it would otherwise be impossible.

Modern vehicles use automated driving assistance systems (ADAS) products to automate certain aspects of driving, which improves operational safety. In the U.S. in 2020, 38,824 fatalities occurred due to automotive accidents, and typically about 25% of these are associated with inclement weather. ADAS features have been shown to reduce potential collisions by up to 21%, thus reducing overall accidents. But ADAS typically utilize camera sensors that rely on lane visibility and the absence of obstructions in order to function, rendering them ineffective in inclement weather. To address this research gap, we propose a new technique to estimate snow coverage so that existing and new ADAS features can be used during inclement weather. In this study, we use a single camera sensor and historical weather data to estimate snow coverage on the road. Camera data was collected over 6 miles of arterial roadways in Kalamazoo, MI.

Connected and Automated Vehicles (CAV) provide new prospects for energy-efficient driving due to their improved information accessibility, enhanced processing capacity, and precise control. The idea of the Eco-Driving (ED) control problem is to perform energy-efficient speed planning for a connected and automated vehicle using data obtained from high-resolution maps and Vehicle-to-Everything (V2X) communication. With the recent goal of commercialization of autonomous vehicle technology, more research has been done to the investigation of autonomous eco-driving control. Previous research for autonomous eco-driving control has shown that energy efficiency improvements can be achieved by using optimization techniques. Most of these studies are conducted through simulations, but many more physical vehicle integrated test application studies are needed.

Robust sensor fusion is a key technology for enabling the safe operation of automated vehicles. Sensor fusion typically utilizes inputs of cameras, radars, lidar, inertial measurement unit, and global navigation satellite systems, process them, and then output object detection or positioning data. This paper will focus on sensor fusion between the camera, radar, and vehicle wheel speed sensors which is a critical need for near-term realization of sensor fusion benefits. The camera is an off-the-shelf computer vision product from MobilEye and the radar is a Delphi/Aptive electronically scanning radar (ESR) both of which are connected to a drive-by-wire capable vehicle platform. We utilize the MobilEye and wheel speed sensors to create a dynamic region of interest (DROI) of the drivable region that changes as the vehicle moves through the environment.

Today’s Advanced Driver Assistance Systems (ADAS) predominantly utilize cameras to increase driver and passenger safety. Computer vision, as the enabler of this technology, extracts two key environmental features: the drivable region and surrounding objects (e.g., vehicles, pedestrians, bicycles). Lane lines are the most common characteristic extracted for drivable region detection, which is the core perception task enabling ADAS features such as lane departure warnings, lane-keeping assistance, and lane-centering. However, when subject to adverse weather conditions (e.g., occluded lane lines) the lane line detection algorithms are no longer operational. This prevents the ADAS feature from providing the benefit of increased safety to the driver. The performance of one of the leading computer vision system providers was tested in conditions of variable snow coverage and lane line occlusion during the 2020-2021 winter in Kalamazoo, Michigan.

Automotive companies are constantly looking to increase the fuel efficiency, shift quality, passenger comfort, and to reduce wear and tear on the components. Most of these aspects depend on the accuracy of torque used for transmission control, which determines the required operational gear position at a given speed and road conditions. Currently, SAE J-1939 CAN bus torque estimation relies on steady state maps that are generated during the calibration of the engine for different speeds and loads. In this paper we report the development of a Virtual Flywheel Torque Sensor (VFTS) useful for real time torque measurement based on an engine speed harmonics analysis. The VFTS uses a signal from the flywheel speed sensor to estimate the flywheel angular acceleration, which and provides a proportional torque value which corresponds to torque at the flywheel.

The transportation sector contributes significantly to emissions and air pollution globally. Emission models of modern vehicles are important tools to estimate the impact of technologies or controls on vehicle emission reductions, but developing a simple and high-fidelity model is challenging due to the variety of vehicle classes, driving conditions, driver behaviors, and other physical and operational constraints. Recent literature indicates that neural network-based models may be able to address these concerns due to their high computation speed and high-accuracy of predicted emissions. In this study, we seek to expand upon this initial research by utilizing several deep neural networks (DNN) architectures such as a recurrent neural network (RNN) and a convolutional neural network (CNN). These DNN algorithms are developed specific to the vehicle-out emissions prediction application, and a comprehensive assessment of their performances is done.

While machine learning in autonomous vehicles development has increased significantly in the past few years, the use of reinforcement learning (RL) methods has only recently been applied. Convolutional Neural Networks (CNNs) became common for their powerful object detection and identification and even provided end-to-end control of an autonomous vehicle. However, one of the requirements of a CNN is a large amount of labeled data to inform and train the neural network. While data is becoming more accessible, these networks are still sensitive to the format and collection environment which makes the use of others’ data more difficult. In contrast, RL develops solutions in a simulation environment through trial and error without labeled data. Our research expands upon previous research in RL and Proximal Policy Optimization (PPO) and the application of these algorithms to 1/18th scale cars by expanding the application of this control strategy to a full-sized passenger vehicle.

Autonomous vehicle technology has the potential to improve the safety, efficiency, and cost of our current transportation system by removing human error. With sensors available today, it is possible for the development of these vehicles, however, there are still issues with autonomous vehicle operations in adverse weather conditions (e.g. snow-covered roads, heavy rain, fog, etc.) due to the degradation of sensor data quality and insufficiently robust software algorithms. Since autonomous vehicles rely entirely on sensor data to perceive their surrounding environment, this becomes a significant issue in the performance of the autonomous system. The purpose of this study is to collect sensor data under various weather conditions to understand the effects of weather on sensor data. The sensors used in this study were one camera and one LiDAR. These sensors were connected to an NVIDIA Drive Px2 which operated in a 2019 Kia Niro.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

Developing a complete understanding of the structure and behavior of the near-wall region (NWR) in reciprocating, internal combustion (IC) engines and of its interaction with the core flow is needed to support the implementation of advanced combustion and engine operation strategies, as well as predictive computational models. The NWR in IC engines is fundamentally different from the canonical steady-state turbulent boundary layers (BL), whose structure, similarity and dynamics have been thoroughly documented in the technical literature. Motivated by this need, this paper presents results from the analysis of two-component velocity data measured with particle image velocimetry near the head of a single-cylinder, optical engine. The interaction between the NWR and the core flow was quantified via statistical moments and two-point velocity correlations, determined at multiple distances from the wall and piston positions.

The strong focus on reducing brake drag, driven by a historic ramp-up in global fuel economy and carbon emissions standards, has led to renewed research on brake caliper drag behaviors and how to measure them. However, with the increased knowledge of the range of drag behaviors that a caliper can exhibit comes a particularly vexing problem - how should this complex range of behaviors be represented in the overall road load of the vehicle? What conditions are encountered during coastdown and fuel economy testing, and how should brake drag be measured and represented in these conditions? With the Environmental Protection Agency (amongst other regulating agencies around the world) conducting audit testing, and the requirement that published road load values be repeatable within a specified range during these audits, the importance of answering these questions accurately is elevated. This paper studies these questions, and even offers methodology for addressing them.

The human thermal comfort, which has been a subject of extensive research, is a principal objective of the automotive climate control system. Applying the results of research studies to the practical problems require quantitative information of the thermal environment in the passenger compartment of a vehicle. The exposure to solar radiation is known to alter the thermal environment in the passenger compartment. A photovoltaic-cell based sensor is commonly used in the automotive climate control system to measure the solar radiation exposure of the passenger compartment of a vehicle. The erroneous information from a sensor however can cause thermal discomfort to the occupants. The erroneous measurement can be due to physical or environmental parameters. Shading of a solar sensor due to the opaque vehicle body elements is one such environmental parameter that is known to give incorrect measurement.

The vehicle air-conditioning system has significant impact on fuel economy and range of electric vehicles. Improving the fuel economy of vehicles therefore demand for energy efficient climate control systems. Also the emissions regulations motivate the reduced use of fuel for vehicle's cabin climate control. Solar heat gain of the passenger compartment by greenhouse effect is generally treated as the peak thermal load of the climate control system. Although the use of advanced glazing is considered first to reduce solar heat gain other means such as ventilation of parked car and recirculation of cabin air also have impetus for reducing the climate control loads.