General Motors (Canada)

Software-intensive vehicles require regular over-the-air (OTA) updates. To ensure that OTA updates do not compromise system safety, such updates should be assured. Automotive safety engineers need to efficiently estimate the effort it would take to assure these updates for the entire fleet. To address this challenge, we propose a process for supporting assurance of OTA updates. Our process proposes to model the fleet of vehicles as a software product line (SPL), assured with a variability-aware assurance case. It then measures the difficulty of assuring the proposed update using this variability-aware AC model via a set of heuristics.

Proton exchange membrane fuel cell (PEMFC) is a promising hydrogen technique with various application prospects. However, all the PEMFCs are subject to degradation resulting from mechanical and chemical aging. To tackle this challenge, accurately predicting fuel cell degradation is essential for its durability optimization. In this study, an enhanced data-driven prognostic framework is developed to accurately predict short-term and medium-term degradation using only fuel cell voltage as the input feature. Firstly, a local outlier factor (LOF) algorithm is adopted for automatic detection of outliers in raw data collected from actual sensing environments. Then, an advanced deep learning model, residual–CNN–LSTM-random attention, is proposed to optimize voltage prediction to better indicate future PEMFC degradation trend. The proposed work is validated by the IEEE PHM 2014 Data Challenge. Compared to state-of-the-art methods, the proposed framework provides superior prediction accuracies with high stability. For instance, the framework improves short-term prediction, achieving a root mean square error (RMSE) of 0.0021 and a mean absolute percentage error (MAPE) of 0.0323 at steady state when training stops at 600 h. For medium-term prediction, our method also attains better results with an RMSE of 0.0085 and a MAPE of 0.4237 under same working conditions. Additionally, the comparative analyses demonstrate a lower computational burden and higher suitability of proposed work for practical applications.

Refrigerant-based electric scroll compressors are known for their reliability, efficiency, and quiet operation. They are often used in heat pump systems due to their ability to efficiently handle varying levels of load conditions, both for heating and cooling modes of operation. As electric compressors are considered the heart of the heat pump system, being able to determine degradation of compressors prior to failure is of paramount importance for the health of this system. Typical failures for electric scroll compressors range from electrical faults, refrigerant leaks, to mechanical failures and overheating. Specifically, one of the primary failure modes for an electric scroll compressor is mechanical damage due to the high stress effects of refrigerant liquid slugging. These stresses are due to excessively high internal pressures exhibited on the compressor scrolls, which are generated by compressing liquid refrigerant at the suction side of the compressor. This paper provides a new testing methodology that introduces liquid slugging at various degrees of refrigerant quality to degrade a compressor to near the end of useful life. Furthermore, this test aims to determine specific operating conditions and signals that can indicate early compressor degradation. This fault injection configuration consists of a modified heat pump system with the addition of two low pressure heat exchangers added in parallel (with respective electronically controlled expansion valve for each heat exchanger) used to control the refrigerant quality during compressor operations. For a given refrigerant quality, the heat pump system was operated at a fixed compressor performance conditions to sustain liquid slugging for a fixed duration. Afterwards, refrigerant was controlled to be pure vapor at the compressor suction side and the compressor was controlled at several different performance conditions (i.e., fixed compressor suction superheat temperature and compressor pressure ratios, at various compressor speeds), so as to duplicate conditions known to us from the compressor component data sheet for an ideal electric scroll compressor. Through these tests, the results show that the severity of scroll failures depend heavily on the refrigerant quality and the amount of liquid slugging exposure time. Furthermore, symptoms of compressor degradation are detected using the following signals: i) temperature and pressure at the compressor suction side, ii) temperature and pressure at the compressor discharge side, and iii) electric compressor speed and power consumption. To further aid in determining the compressor degradation ground truth, complete compressor teardown was performed to identify sections within the compressor that exhibited significant amounts of wear as compared to a stock compressor.

Machine automation is often faster, more repeatable, precise, and reliable than human reflexes. Humans as machine operators are also prone to distraction, fatigue, and cognitive overload. World Health Organization (WHO) considered human error as the single largest cause of all collisions today and a contributing factor in more than 90% of fatal crashes [1]. Minimizing the role of human operator is one of the motivations for the developments in automation, both in air and ground transportation. While the role of autopilot in aviation is both a precursor and inspiration for self-driving technology in ground transportation, there are significant differences between the two. This paper sheds lights on similarities, differences, and potential synergies between automation in aviation and ground transportation.

In a world with increasing numbers of robots operating in everyday human spaces, the employees at this robotics company are pioneers, with intelligent point-to-point path planning and autonomous transport operations in 150+ factory and warehouse locations in North America. At the time of research, this robotics company consisted of 250 employees. Unlike other industry models, their robots are designed to operate with people in mixed human-machine spaces, yet no HRI style evaluations had previously been run with their robots. As early observers of how factory workers and transport robot interact, across varied job roles ranging from technology design to customer relations, this work sought to leverage employee knowledge and experiences to identify opportunities for improving the communication capabilities of the robots, resulting in the addition of several robot state communications to their initial software set leveraging both employee- and social robotics literature- sourced ideas for communicating with lights. To achieve this a social robotics researcher spent a summer onsite at the robotics company, getting to know their software stack and culture. Her research activities included: (1) a company-wide survey relative to the robot’s light, sound, and motion communications was sent out and analyzed, (2) the development of three new light sets (car-like, sweeping, heartbeat) and five overall states (blocked, at goal, turning, idle), and (3) a user study evaluating the developed light sets relative to the current robot default light patterns, all significantly improving the overall legibility of the targeted robot state communications: at goal, blocked, turning, and idle. Our initial findings advance knowledge in which style of light patterns is best for different communication states, showing that eye-catching lights are best for high urgency states, such as blocked, and subtle lights are best for low urgency states, such as idle. Finally, the latest software release for this robot has deployed a subset of these light patterns to all of their currently operating client sites, i.e., anyone who updates their robots to the latest release will benefit from these research results. This deployment sets the ground for future researchers exploring how end-users at different sites have responded to the new, more communicative light patterns.

This study presents a framework to assess the effectiveness of various health indicators (HIs) used to monitor the state of health (SOH) of a brake rotor health monitoring system. The following criteria were used to rank various health indicators: (i) Identifiability: Correlation of the HI with the Ground Truth (GT); (ii) Compactness: Mean of the standard deviation of the estimated SOHs; (iii) Robustness to Noise Factors: An HI is considered robust when it meets all functional and customer requirements under all operating conditions and its performance is not affected by the variations in the environment, operating conditions or other factors impacting the performance in an undesired way (noise factors); (iv) Monotonicity: To quantify the monotonic trend in HIs as the fault level increases from healthy baseline to the most severe faults. Monotone HIs are preferred as they will likely generalize better to data not used in development; and (v) Estimation Error: The average relative error between the GT and the prediction obtained from the regression analysis. Results showed that this framework can be applied to several HIs derived from performing time and frequency analysis on various sensor signals used to monitor the health of brake rotors. Top HIs selected based on this framework provided the best performance in detecting degraded brake rotors as evidenced by higher classification score.

Bearing condition monitoring is a widely studied field, but applications to the automotive industry have received little attention as the bearing failure rates are typically low in traditional internal combustion engine vehicles with 200 – 300k mile lifespans. The rapid advancement of electric and autonomous vehicles enables vehicles with million-mile lifespans. This implies that the reliable life of existing bearing designs is exceeded throughout the vehicle life, which can potentially lead to vehicle failure. To enable the development of a bearing fault detection and prognostics system, healthy and faulty bearing data must be collected, and the ground-truth states of the health of bearings need to be determined for algorithm refinement and validation. This work explores the fault injecting options, and ground-truthing together with their limitations. Two methods based on precision machining and seeded spalling are developed and used to inject inner race faults in a ball bearing. A non-invasive ground-truthing method is proposed to quantify the state of health of the fault injected bearings in which bench test data is collected under various speed and load conditions. The vibration signals from the bench tests are used to calculate the root-square of the area under the acceleration Power Spectral Density curve (known as GRMS) for each speed and load condition. To remove the dependency of the results on load and speed conditions, a speed-load-GRMS plot is generated, and a plane is fitted to the data for each fault level. Next, the volume under the plot is calculated, yielding a single cumulative GRMS value for each fault level. This value is used as the ground-truth health of bearing for each fault level. For the bearing with the faults injected using precision machining fault injection, the obtained ground-truth values are 1.56, 3.68, and 4.36 times larger than the same figure for the healthy bearing for the faults with the widths of 0.1 mm, 0.5 mm, and 2 mm, respectively. The observed correlation between the fault sizes and the calculated ground-truth values validates the proposed method which can provide a good separation among different health states of a bearing.

In this paper, a new approach for lane change and double-lane change planning and following for autonomous driving is proposed. Herein, we introduce a novel technique, based on exponential functions, to generate online and feasible lane change maneuvers; these maneuvers satisfy the constraints on the maximum allowable curvature a given vehicle can handle. In addition, a simultaneous local path planning and path-following control framework is adapted. The framework utilizes a multi-threading architecture to run the local planner module concurrently with the control module. The planning module generates parametric reference paths based on the proposed lane change and double-lane change maneuvers. The control module is based on a Model Predictive Path-following Control (MPFC) scheme, which achieves the path following objective while satisfying vehicle’s state and control limits. To validate the proposed framework, several real-time simulation scenarios are designed and tested on CARLA soft real-time vehicle simulator. The results show the effectiveness of the proposed framework in generating and smoothly following lane change maneuvers.

This paper presents a new lateral control structure for path following control in an autonomous vehicle. The proposed method transforms a conventional feedback path following controller to a control architecture that relies exclusively on feedforward control of the curvature at a lookahead point of a blend path that connects the current vehicle kinematics with the desired target trajectory at some merge point. This approach provides a practical method for ensuring tracking performance and passenger comfort. Mathematical proofs and experiment results are provided to demonstrate that the proposed controller guarantees feedback stability of the lateral error dynamics, provides a systematic way of setting the corresponding feedback gains for accurate path tracking, and delivers desirable transient characteristics and smooth steering actuation effort.

Accidents and emergency situations have been on a constant rise, especially during the COVID-19 pandemic. Typically, the emergency vehicle dispatch and routing problems involve various dynamic factors which make them very different from conventional vehicle routing problems. This paper presents a metaheuristic approach to emergency vehicle dispatch and routing. Dispatching aims at allotting and sending the nearby available vehicle to the location of emergency and routing deals with selecting the ideal route to reach the destination. The objective is to minimize incident response time and the total time travel for the vehicle from the dispatch point to the destination. This usually depends on the emergency service vehicle availability and other dynamic factors such as traffic, number of turns in the route, etc. Three different bio-inspired algorithms, namely, ant colony optimization, adaptive ACO and firefly algorithm are investigated. Performance evaluation shows that firefly algorithm outperforms the other algorithms in terms of cost, number of turns, and run time for the given data set. However, in case of larger datasets and multiple variables if involved, adaptive ACO gives better results but takes longer time.