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Abstract In the automotive domain, the overall complexity of technical components has increased enormously. Formerly isolated, purely mechanical cars are now a multitude of cyber-physical systems that are continuously interacting with other IT systems, for example, with the smartphone of their driver or the backend servers of the car manufacturer. This has huge security implications as demonstrated by several recent research papers that document attacks endangering the safety of the car. However, there is, to the best of our knowledge, no holistic overview or structured description of the complex automotive domain. Without such a big picture, distinct security research remains isolated and is lacking interconnections between the different subsystems. Hence, it is difficult to draw conclusions about the overall security of a car or to identify aspects that have not been sufficiently covered by security analyses.

Abstract The use of nanomaterials and nanostructures have been revolutionizing the advancements of science and technology in various engineering and medical fields. As an example, Carbon Nanotubes (CNTs) have been extensively used for the improvement of mechanical, thermal, electrical, magnetic, and deteriorative properties of traditional composite materials for applications in high-performance structures. The exceptional materials properties of CNTs (i.e., mechanical, magnetic, thermal, and electrical) have introduced them as promising candidates for reinforcement of traditional composites. Most structural configurations of CNTs provide superior material properties; however, their geometrical shapes can deliver different features and characteristics. As one of the unique geometrical configurations, helical CNTs have a great potential for improvement of mechanical, thermal, and electrical properties of polymeric resin composites.

Abstract This study investigates methods to precisely control a coolant pump in an internal combustion engine. The goal of this research is to minimize power consumption while still meeting optimal performance, reliability and durability requirements for an engine at all engine-operating conditions. This investigation achieves reduced fuel consumption, reduced emissions, and improved powertrain performance. Secondary impacts include cleaner air for the earth, reduced operating costs for the owner, and compliance with US regulatory requirements. The study utilizes mathematical modeling of the cooling system using heat transfer, pump laws, and boiling analysis to set limits to the cooling system and predict performance changes.

Abstract This paper presents a machine learning application of the force/torque sensor in a human-robot collaborative manufacturing scenario. The purpose is to simplify the programming for physical interactions between the human operators and industrial robots in a hybrid manufacturing cell which combines several robotic applications, such as parts manipulation, assembly, sealing and painting, etc. A multiclass classifier using Light Gradient Boosting Machine (LightGBM) is first introduced in a robotic application for discriminating five different contact states w.r.t. the force/torque data. A systematic approach to train machine-learning based classifiers is presented, thus opens a door for enabling LightGBM with robotic data process. The total task time is reduced largely because force transitions can be detected on-the-fly. Experiments on an ABB force sensor and an industrial robot demonstrate the feasibility of the proposed method.

Abstract To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time.

Abstract Modern vehicles have incorporated numerous safety-focused advanced driver-assistance systems (ADAS) in the last decade including smart cruise control and object avoidance. In this article, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy (FE). We combine ADAS sensor data with a previously developed prediction model, dynamic programming (DP) optimal energy management control, and a validated model of a 2010 Toyota Prius to explore FE. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real-world city and highway drive cycles in Denver, Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various ADAS algorithms are combined, modified, and compared to the ground truth results.

Abstract Under contract to the EPA, Eastern Research Group analyzed light-duty vehicle OBD monitor readiness and diagnostic trouble codes (DTCs) using inspection and maintenance (I/M) data from four states. Results from roadside pullover emissions and OBD tests were also compared with same-vehicle I/M OBD results from one of the states. Analysis focused on the evaporative emissions control (evap) system, the catalytic converter (catalyst), the exhaust gas recirculation (EGR) system and the oxygen sensor and oxygen sensor heater (O2 system). Evap and catalyst monitors had similar overall readiness rates (90% to 95%), while the EGR and O2 systems had higher readiness rates (95% to 98%). Approximately 0.7% to 2.5% of inspection cycles with a “ready” evap monitor had at least one stored evap DTC, but DTC rates were under 1% for the catalyst and EGR systems, and under 1.1% for the O2 system, in the states with enforced OBD programs.

Abstract A critical component of vehicle dynamic control systems is the accurate and real-time knowledge of the vehicle’s key states and parameters when running on the road. Such knowledge is also essential for vehicle closed-loop feedback control. Vehicle state and parameter estimation has gradually become an important way to soft-sense some variables that are difficult to measure directly using general sensors. In this work, a seven degrees-of-freedom (7-DOF) nonlinear vehicle dynamics model is established, where consideration of the Magic formula tire model allows us to estimate several vehicle key states using a hybrid algorithm containing an unscented Kalman filter (UKF) and a genetic algorithm (GA). An estimator based on the hybrid algorithm is compared with an estimator based on just a UKF. The results show that the proposed estimator has higher accuracy and fewer computation requirements than the UKF estimator.

Abstract Reactivity-controlled compression ignition (RCCI) is a dual fuel low temperature combustion (LTC) strategy which results in a wider operating load range, near-zero oxides of nitrogen (NOx) and particulate matter (PM) emissions, and higher thermal efficiency. One of the major shortcomings in RCCI is a higher unburned hydrocarbon (HC) and carbon monoxide (CO) emissions. Unlike conventional combustion, aftertreatment control of HC and CO emissions is difficult to achieve in RCCI owing to lower exhaust gas temperatures. In conventional RCCI, an early direct injection (DI) of low volatile diesel fuel into the premixed gasoline-air mixture in the combustion chamber results in charge stratification and fuel spray wall wetting leading to higher HC and CO emissions. To address this limitation, a homogeneous charge reactivity-controlled compression ignition (HCRCCI) strategy is proposed in the present work, wherein the DI of diesel fuel is eliminated.

Abstract Fan cooling system of an air-cooled diesel engine is optimized using 3D CFD numerical simulation approach. The main objective of this article is to increase engine fuel efficiency by reducing fan power consumption. It is achieved by optimizing airflow rates and flow distribution over the engine surfaces to keep the maximum temperature of engine oil and engine surfaces well within the lubrication and material limit, respectively, at the expense of lower fan power. Based on basic fan laws, a bigger fan consumes lesser power for the same airflow rate as compared to a smaller fan, provided both fans have similar efficiency. Flow analysis is also conducted with the engine head and block modeled as solid medium and fan cooling system as fluid domain. Reynolds-averaged Navier-Stokes turbulence (RANS) equations were solved to get the flow field inside the cooling system and on the engine liner fins. The Moving Reference Frame approach was used for simulating the rotation of a fan.

Abstract The development of automated driving systems (ADS) necessitates procedures to validate system safety. The reliability of an ADS’s environment perception provided by lidar, radar, and camera sensors is of special interest in this context, because perception errors can be safety-critical. In this article, we formalize the reliability-based validation of environment perception for safe automated driving and discuss associated challenges. We describe a potential solution to a perception reliability validation by deriving performance requirements at the sensor level. We then summarize statistical methods to learn sensor perception reliabilities in field tests, on proving grounds, and through virtual simulations. With the developed safety validation framework, we show that, potentially, one can validate the safety of an ADS with feasible test effort.

Abstract In recent years, to avoid the failure of the onboard perception system, intelligent vehicle infrastructure cooperative systems have been attracting attention in the field of autonomous vehicles. Using the perception technology of roadside sensors to provide supplementary traffic information for autonomous vehicles has become an increasing trend. Several roadside perception solutions select deep learning for three-dimensional (3D) object detection. However, deep learning methods have several issues and lack reliability in practical engineering applications. To tackle this challenge, this study proposes a pedestrian detection algorithm based on roadside Light Detection And Ranging (LiDAR) by combining traditional and deep learning algorithms. To meet real-time demand, Octree with region-of-interest (ROI) selection is introduced and improved to filter the background in each frame, which improves the clustering speed.

Abstract Weather conditions such as rain, fog, snow, and dust can adversely impact sensing and perception, limit operational envelopes, and compromise the safety and reliability of advanced driver-assistance systems and autonomous vehicles. Physical testing of an autonomous system in a weather laboratory and on-road is costly and slow and exposes the system to only a limited set of weather conditions. To overcome the limitations of physical testing, a physics-based simulation workflow was developed by coupling computational fluid dynamics (CFD) with optical simulations of camera and lidar sensors. The computational data of various weather conditions can be rapidly generated by CFD and used to assess the impact of weather conditions on the sensors and perception algorithms.

Abstract Currently, the governments are encouraging automotive vehicle manufacturers to produce electric vehicles (EVs) as these vehicles have a zero-emission footprint. Generally, the EVs are expected to be quieter compared to internal combustion engine (ICE) vehicles. But the absence of engine noise in EVs brings more challenges for noise, vibration, and harshness (NVH) as the other noise sources become more audible. Most of these noise sources are tonal in nature and, hence, cause discomfort to the passengers. The present work is related to the noise refinement in a pure EV. The dominant noise sources observed in this vehicle are the electric powertrain, cooling fan, and air compressor. The powertrain consists of a traction motor and a gearbox (GB) with a planetary gear system. The root cause identification of electric powertrain noise has been investigated with masking trials and with the acoustic camera.

Abstract HVAC systems of passenger cars and especially their air purification performance got more and more in focus during the last years. One reason is the overall increased attention to air quality and its effect on human health. Recently, the WHO further tightened the recommended values for many pollutants. This will likely intensify the trend to more complex systems for improving the air purification functionalities. But, up to now there is no standard method for air purification performance testing. Existing standards cover the vehicle cabin air quality only regarding material emissions. Several studies address assessing the performance of air purification functionalities in most cases by real driving tests typically performed in urban areas. This approach results in proper values for the basic efficiency of single systems.

Abstract Vehicle interior air quality is usually determined by the levels of in-cabin air pollutants, such as particulate matter (PM), gaseous air pollution (volatile organic compounds [VOCs], oxides of nitrogen [NOx], and carbon monoxide [CO]), and carbon dioxide [CO2], which reflect the freshness of indoor air. Nowadays, cabin air filters play a key role in preventing outdoor air pollutants transporting inside vehicles; hence, in-cabin air quality can be strongly associated with the filtration performance of cabin air cleaning solutions. However, challenges are existing in a standard method for assessing the performance of a cabin air filter in real-life driving conditions. This study is to develop a low-cost mobile test method for monitoring in-vehicle PM and CO2 and evaluating the performances of cabin air filters while driving the vehicles. The results reveal that certain boundary conditions are important to have a proper method for evaluating the particle removal efficiency.

Abstract This article presents data mining methodologies designed to support data-driven, long-term, and large-scale research in the areas of in-vehicle monitoring, learning, and assessment of older adults’ driving behavior and physiological signatures under a set of well-defined driving scenarios. The major components presented in the article include the instrumentation of an easily transportable vehicle data acquisition system (VDAS) designed to collect multimodal sensor data during naturalistic driving, an ontology that enables the study of driver behaviors at different levels of integration of semantic heterogeneity into the driving context, and a driving trip segmentation algorithm for automatically partitioning a recorded real-world driving trip into segments representing different types of roadways and traffic conditions.

Abstract Safety is always a crucial aspect of developing autonomous systems, and the motivation behind this project comes from the need to address the traffic crashes occurring globally on a daily basis. The present work studies the coexistence of the novel rule-based behavioral planning framework with the five key advanced driver assistance system (ADAS) features as proposed in this article to fulfill the safety requirements and enhance the comfort of the driver/passengers to achieve a receding-horizon autopilot. This architecture utilizes data from the sensor fusion and the prediction module for the prediction time horizon of 2 s iteratively, which is continuously moving forward (hence, the receding horizon), and helps the behavior planner understand the intent of other vehicles on the road in advance.

Abstract In the past years, the automotive industry has been integrating multiple hardware in the vehicle to enable new features and applications. In particular automotive applications, it is important to monitor the actions and behaviors of drivers and passengers to promote their safety and track abnormal situations such as social disorders or crimes. These applications rely on multiple sensors that generate real-time data to be processed, and thus, they require adequate data acquisition and analysis systems. This article proposes a prototype to enable in-vehicle data acquisition and analysis based on the middleware framework Robot Operating System (ROS). The proposed prototype features two processing devices and enables synchronized audio and video acquisition, storage, and processing. It was assessed through the implementation of a live inference system consisting of a face detection algorithm from the data gathered from the cameras and the microphone.

Abstract For safe and comfortable automated driving in the urban domain, especially in complex geometries as intersections, the prediction of surrounding traffic participants is fundamental. Several works in this field focus on predicting the behavior of vulnerable road users (VRU) at crossings. However, no approaches were found dealing with predicting the interaction between turning vehicles giving right of way or cooperating with VRU, which is substantial for the trajectory planning of following vehicles. Infrastructural sensor data from an intersection in Germany enables the development of a prediction concept for vehicles interacting with VRU. Our studies show that the original criteria for classifying an interaction between vehicles and VRU—the post-encroachment time (PET)—is not suitable as ground truth criteria for the aimed prediction. Instead, a clustering-based labelling approach with k-means shows promising results in trajectory pattern distinction.