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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.

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.

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 autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm.

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.

Practical applications of recently developed sensor fusion algorithms perform poorly in the real world due to a lack of proper evaluation during development. Existing evaluation metrics do not properly address a wide variety of testing scenarios. This issue can be addressed using proactive performance measurements such as the tools of resilience engineering theory rather than reactive performance measurements such as root mean square error. Resilience engineering is an established discipline for evaluating proactive performance on complex socio-technical systems which has been underutilized for automated vehicle development and evaluation. In this study, we use resilience engineering metrics to assess the performance of a sensor fusion algorithm for vehicle localization. A Kalman Filter is used to fuse GPS, IMU and LiDAR data for vehicle localization in the CARLA simulator.

Standard operation of autonomous vehicles on public roads results in significant exposure to high levels of risk. There is a significant need to develop metrics that evaluate safety of an automated system without reliance on the rate of vehicle accidents and fatalities compared to the number of miles driven; a proactive rather than a reactive metric is needed. Resilience engineering is a new paradigm for safety management that focuses on evaluating complex systems and their interaction with the environment. This paper presents the overall methodology of resilience engineering and the resilience assessment grid (RAG) as an evaluation tool to measure autonomous systems' resilience. This assessment tool was used to evaluate the ability to respond to the system. A Pure Pursuit controller was developed and utilized as the path tracking control algorithm, and the Carla simulator was used to implement the algorithm and develop the testing environment for this methodology.

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.

Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate.

The paper presents a probability density function (PDF)-based tool that can be used to guide the identification of the stationary state of computational fluid dynamics (CFD) simulation results. Specifically, the probability density distributions of local lube oil volume fractions (VFs) are used. Applications of the tool to the time-dependent turbulent flow of lube oil in a gearbox are reported. The CFD simulations were performed using a traditional finite volume method and a particle-based method, respectively. The rotational speeds were 3000 rpm and 6535.7 rpm for the driving and the driven helical gears, and the lubricant’s temperature was 37.5°C. Besides lube oil flow behavior and VFs, also discussed are flow patterns, churning-loss predictions, and individual contributions from the two gears.

Due to the recent advancements in autonomous vehicle technology, future vehicle velocity predictions are becoming more robust, which allows fuel economy (FE) improvements in hybrid electric vehicles (HEVs) through optimal energy management strategies (EMS). Velocity predictions generated between 5 and 30 s predictions could be implemented using model predictive control (MPC), but the performance of MPC must be well understood. Also, the vulnerability of predictive optimal EMS to velocity prediction accuracy should be addressed. Before an optimal EMS can be implemented, its overall performance must be evaluated and benchmarked against relevant velocity prediction metrics. A real-world highway drive cycle (DC) in the high-fidelity, controls-oriented 2017 Toyota Prius Prime model operating in charge-sustaining mode was utilized to observe FE realization.

This paper describes the work performed in predicting and measuring the contribution of oil churning to the no-load losses of a commercial truck axle at typical running speeds. A computational fluid dynamics (CFD) analysis of the churning losses was conducted. The CFD model accounts for design geometry, operating speed, temperature, and lubricant properties. The model calculates the oil volume fraction and the torque loss caused by oil churning due to the viscous and inertia effects of the fluid. CFD predictions of power losses were then compared with no-load measurements made on a specially developed, dynamometer-driven test stand. The same axle used in the CFD model was tested in three different configurations: with axle shafts, with axle shafts removed, and with ring gear and carrier removed. This approach to testing was followed to determine the contribution of each source of loss (bearings, seals, and churning) to the total loss.

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.

Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado.

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.

This paper proposes a transmission ratio scheduling and control methodology for a vehicle with a Continuous Variable Transmission (CVT) and a downsized gasoline engine. The methodology is designed to deliver the optimal vehicle fuel economy within drivability and performance constraints. Traditionally, the Optimum Operating Line (OOL) generated from an engine brake specific fuel consumption map is considered to be the best option for ratio scheduling, as it defines the points at which engine efficiency is maximized. But the OOL does not consider transmission efficiency, which may be a source of significant losses. To develop a CVT ratio schedule that offers the best fuel economy for the complete powertrain, an empirical approach was used to minimize fuel consumption by considering engine efficiency, CVT efficiency, and requested vehicle power. A backward-looking model was used to simulate a standard driving cycle (FTP-75) and develop a new powertrain-optimal operating line (P-OOL).

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.

Fatigue damage sensing and monitoring of any structure is a prerequisite for reliable and effective structural health diagnosis. The designed sensor has alternate slots and strips with different strain magnification factor with respect to the nominal strain at its location. The strips experience the strains which closely resemble the actual strain distribution in the critical area of the component. One of the major advantages of this sensor is that it can be placed at any convenient location, still experiencing the same fatigue damage as a critical location. It can be used on various structures from ground civilian and military vehicles to steel bridges. This can predict the remaining useful life of a component or the number of miles (for any automobile) left for the component before it needed replacement. This paper mainly describes the design aspects of this sensor following analytical and finite element analysis (FEA) approaches.

Aerodynamic performance testing of heavy duty fans involve complicated test setups with specialized equipment and measurement systems as summarized in ANSI 210-07 standard. This paper describes virtual testing and simulation methods to obtain the fan aerodynamic performance data using commercial Computational Fluid Dynamics (CFD) software. Two different virtual test environments were used during the analysis. The first one is a virtual test chamber which is constructed based on the actual fan system installation. The second one is a virtual flow tube which approximates a fan flow test set-up as outlined in ANSI 210-07. The virtual fan is created from (i) the laser scan of the actual fan and (ii) the design specifications of the fan. The virtual test conditions simulate the actual test arrangement by imposing free boundary at flow inlet/outlet and proper fan rotation. The aerodynamic flow rate is controlled by a variable orifice located at the virtual test chamber outlet.