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"Spotlight on Design" features video interviews and case study segments, focusing on the latest technology breakthroughs. Viewers are virtually taken to labs and research centers to learn how design engineers are enhancing product performance/reliability, reducing cost, improving quality, safety or environmental impact, and achieving regulatory compliance. In the episode "Automated Vehicles: Sensors and Future Technologies" (24:31), highly automated driving is looked at in detail as the culmination of years of research in automotive technology, sensors, infrastructure, software, and systems integration. Real-life case studies show how organizations are actually developing solutions to the challenge of making cars safer with less driver intervention. IAV Automotive Engineering demonstrates how a highly automated vehicle capable of lane changing was created.

"Spotlight on Design: Insight" features an in-depth look at the latest technology breakthroughs impacting mobility. Viewers are virtually taken to labs and research centers to learn how design engineers are enhancing product performance/reliability, reducing cost, improving quality, safety or environmental impact, and achieving regulatory compliance. Automated driving is made possible through the data acquisition and processing of many different kinds of sensors working in unison. Sensors, cameras, radar, and lidar must work cohesively together to safely provide automated features. In the episode "Automated Vehicles: Converging Sensor Data" (8:01), engineers from IAV Automotive Engineering discuss the challenges associated with the sensor data fusion, and one of Continental North America’s technical teams demonstrate how sensors, radars, and safety systems converge to enable higher levels of automated driving.

Active Safety & Advanced Driver Assistance Systems help prevent accidents or mitigate accident severity. Some of these safety systems provide alerts to the driver in critical situations, while others respond to threats by automatically braking and steering the vehicle to avoid crashes. This technical paper collection covers the latest technologies in active safety and driver assistance systems.

Papers in the collection focus on Advanced Driver Assistance Systems (ADAS), which are gaining major importance all vehicle segments. The effectiveness of these systems is based upon the ability to not only sense the outside world, but also the ability to use the information intelligently.

Active Safety and Driver assistance systems are gaining importance as many passive safety systems have already been found to have yielded significant safety benefits that are possible from the deployment of those systems in the fleet. Similar success will much depend upon how fast these systems proliferate the entire passenger vehicle fleet. It will also depend on the deployment strategies used by the industry and the government as well as consumer acceptance and market demand for these systems. Additionally, opportunities exist to use the information gained from the various onboard sensors and vision systems in active safety systems for improving the effectiveness of today’s passive safety systems such as seat belts, airbags, and post-crash safety systems even further by the integration of active and passive safety systems.

Papers in the collection focus on Advanced Driver Assistance Systems (ADAS), which are gaining major importance all vehicle segments. The effectiveness of these systems is based upon the ability to not only sense the outside world, but also the ability to use the information intelligently.

Abstract Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. However, modeling driver’s lane-changing decision and implementation process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, this article presents a personalized Lane-Changing Model (LCM) for Advanced Driver Assistance System (ADAS) based on deep learning method. The LCM contains three major computational components. Firstly, with abundant inputs of Root Residual Network (Root-ResNet), LCM is able to exploit more local information from the front view video data. Secondly, the LCM has an ability of learning the global spatial-temporal information via Temporal Modeling Blocks (TMBs). Finally, a two-layer Long Short-Term Memory (LSTM) network is used to learn video contextual features combined with lane boundary based distance features in lane change events.

Abstract This study aims to take the first step in bridging the gap between vehicle dynamics systems and autonomous control strategies research. More specifically, a nested method is employed to evaluate the collision avoidance ability of autonomous vehicles in the primary design stage theoretically based on both dynamics and control parameters. An integrated model is derived from a half car mathematical model in the lateral direction, consisting of two degrees of freedom, lateral deviation and yaw angle, with a traction mathematical model in the longitudinal direction, consisting of two degrees of freedom, the longitudinal velocity and rolling velocity of the wheel. The integrated model uses a mathematical power train model to generate the torque on the wheel and connects the two systems via the magic formula tyre model to represent the tyre non-linearity during augmented longitudinal and lateral dynamic attitudes.

Abstract Real-time reconstruction of 3D environment attributed with semantic information is significant for a variety of applications, such as obstacle detection, traffic scene comprehension and autonomous navigation. The current approaches to achieve it are mainly using stereo vision, Structure from Motion (SfM) or mobile LiDAR sensors. Each of these approaches has its own limitation, stereo vision has high computational cost, SfM needs accurate calibration between a sequences of images, and the onboard LiDAR sensor can only provide sparse points without color information. This paper describes a novel method for traffic scene semantic segmentation by combining sparse LiDAR point cloud (e.g. from Velodyne scans), with monocular color image. The key novelty of the method is the semantic coupling of stereoscopic point cloud with color lattice from camera image labelled through a Convolutional Neural Network (CNN).

Abstract Obstacle avoidance is an important function in self-driving vehicle control. When the vehicle move from any arbitrary start positions to any target positions in environment, a proper path must avoid both static obstacles and moving obstacles of arbitrary shape. There are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. In this paper reinforcement learning is applied to the problem to form effective strategies. There are two major challenges that make self-driving vehicle different from other robotic tasks. Firstly, in order to control the vehicle precisely, the action space must be continuous which can’t be dealt with by traditional Q-learning. Secondly, self-driving vehicle must satisfy various constraints including vehicle dynamics constraints and traffic rules constraints. Three contributions are made in this paper.

Abstract ISO 26262 needs to be considered when developing safety-relevant E/E systems within the automotive industry. One part of the development process according to ISO 26262 is the derivation of the safety requirements for component functions. Here, one attribute of the safety requirements is the Automotive Safety Integrity Level (ASIL). The ASIL at a component level can be determined using ASIL allocation and decomposition. Considering complex systems such as vehicle electrical systems, countless possibilities can be identified for how the ASILs at a component level can be assigned in line with safety goals. In terms of efficiency, manual assignment is not expedient. Therefore, an algorithm for automated assignment of the ASILs will be introduced which considers constraints based on a fault tree analysis. The function of the approach will be demonstrated using the example of a vehicle electrical system from an automated vehicle.

Abstract The main focus of this article is the online estimation of the terminal airspace sector capacity from the Air Traffic Controller 0ATC) dynamical neural model using Neural Partial Differentiation (NPD) with permissible safe separation and affordable workload. For this purpose, a primarily neural model of a multi-input-single-output (MISO) ATC dynamical system is established, and the NPD method is used to estimate the model parameters from the experimental data. These estimated parameters have a less relative standard deviation, and hence the model validation results show that the predicted neural model response is well matched with the intervention of the ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters, which are unknown in practice.

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 In an automated machining process, monitoring the conditions of the tool is essential for deciding to replace or repair the tool without any manual intervention. Intelligent models built with sensor information and machine learning techniques are predicting the condition of the tool with good accuracy. In this study, statistical models are developed to identify the conditions of the abrasive grinding wheel using the Acoustic Emission (AE) signature acquired during the surface grinding operation. Abrasive grinding wheel conditions are identified using the abrasive wheel wear plot established by conducting experiments. The piezoelectric sensor is used to capture the AE from the grinding process, and statistical features of the abrasive wheel conditions are extracted in time and wavelet domains of the signature. Machine learning algorithms, namely, Classification and Regression Trees (CART) and Support Vector Classifiers (SVC), are used to build statistical models.

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 Management of expressway networks has been mainly focused on defect management without looking at the correlations with accidental risks. This causes unsustainability in expressway infrastructure maintenance since such defects may not be a contributing factor toward public safety. Thus it is necessary to incorporate accidental events for decision-making in infrastructure management. This study has developed a novel approach to machine learning (ML) that incorporates actual primary data from the last 10 years of single-vehicle accidents (SVA) by collisions with motorway facilities, or so-called single-vehicle collisions with fixed objects. The ML is firstly aimed at identifying the influential factors of SVA in relation to finding effective countermeasures for accidents by integrating the correlation analysis, multiple regression analysis, and ML techniques. The study reveals that wet pavement conditions have a significant effect on SVA.

Empirical models for engine-out oxides of Nitrogen (NOx) and smoke emissions have been developed for the purpose of minimizing transient emissions while maintaining transient response. Three major issues have been addressed: data acquisition, data processing and modeling method. Real and virtual transient parameters have been identified for acquisition. Accounting for the phase shift between transient engine events and transient emission measurements has been shown to be very important to the quality of model predictions. Several methods have been employed to account for the transient transport delays and sensor lags which constitute the phase shift. Finally several different empirical modeling methods have been used to determine the most suitable modeling method for transient emissions. These modeling methods include several kinds of neural networks, global regression and localized regression.

This recommended practice applies to vibration monitoring systems for rotorcraft and fixed-wing drive trains, airframes, propulsion systems, electric power generators, and flight control systems. It addresses all aspects of metrics, including what to measure, how to measure, and how to evaluate the results.

The recent interest in IntelliDrive(SM) and vehicle connectivity has gone past previous notions of automatic collision notification, internet browsing and off board navigation. Now, in addition to applying the 5.9 GHz ITS band for cooperative collision avoidance, other applications using connectivity between the vehicle and signalized intersections have emerged. Contemporary applications enhancing fuel economy and reducing green house gases (GHG) are the subject of current research and development. Cooperation between vehicle electronics and intelligent traffic management systems could change the landscape for addressing future ECO (economical and ecological) requirements. This manuscript will outline development within one automotive supplier targeted at using connectivity as an enabling technology for sustaining the vision of smart and green transportation systems.

Active safety systems will have a great impact in the next generation of vehicles. This is partly originated by the increasing consumer's interest for safety and partly by new traffic safety laws. Control actions in the vehicle are based on an extensive environment model which contains information about relevant objects in vehicle surroundings. Sensor data fusion integrates measurements from different surround sensors into this environment model. In order to avoid system malfunctions, high reliability in the interpretation of the situation, and therefore in the environment model, is essential. Hence, the main idea of data fusion is to make use of the advantages of using multiple sensors and different technologies in order to fulfill these requirements, which are especially high due to autonomous interventions in vehicle dynamics (e. g. automatic emergency braking).