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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.

When vehicles share certain information wirelessly via Dedicated Short Range Communications (DSRC), they enable a new layer of electronic vehicle safety that, when needed, can generate warnings to drivers and even initiate automatic preventive actions. Vehicle location and velocity provided by Global Navigation Systems (GNSS), including GPS, are key in allowing vehicle path estimation. GNSS is effective in accurately determining a vehicle's location coordinates in most driving environments, but its performance suffers from obstructions in dense urban environments. To combat this, augmentations to GNSS are being contemplated and tested. This testing has been typically done using a reference GNSS system complimented by expensive military-grade inertial sensors, which can still fail to provide adequate reference performance in certain environments.

We report here results from first year of the BMW MINI E deployment. BMW deployed 450 MINI E?s to North America. Nearly 50% were leased by households in Los Angeles and the New York area. PH&EV Center researchers surveyed MINI E drivers throughout their year with the vehicles, focusing on the experiences of 50 households who volunteered for more detailed interviews. We report here their experiences with driving electric vehicles, adaptions to daily range limitations, and using electricity as a fuel. Presenter Thomas Turrentine, Univ. of California-Davis

ECOtality North America, in partnership with the Idaho National Laboratory (INL), Nissan North America, General Motors, and over 40 government, electric utility, and private organizations, has launched a large-scale demonstration of electric vehicle charging infrastructure. This demonstration, called The EV Project, will deploy more than 15,000 level 2 and DC fast chargers in private residence, commercial, and public locations in seven market areas in Arizona, California, Oregon, Tennessee, Texas, Washington state, and Washington, D.C. The EV Project will also include a total of 5,700 Nissan Leaf battery electric vehicles and 2,600 Chevrolet Volt extended range electric vehicles, operated by consumers and fleets in each of the market areas. This demonstration, which is funded by the U.S. Department of Energy�s (DOE) Vehicle Technologies Program, represents the largest ever deployment of electric vehicles and charging infrastructure.

In Aeronautic industry, when we launch a new industrialization for an aircraft sub assembly we always have the same questions in mind for drilling operations, especially when focusing on lean manufacturing. How can we avoid dismantling and deburring parts after drilling operation? Can a drilling centre perform all the tasks needed to deliver a hole ready to install final fastener? How can we decrease down-time of the drilling centre? Can a drilling centre be integrated in a pulse assembly line? How can we improve environmental efficiency of a drilling centre? It is based on these main drivers that AIRBUS has developed, with SPIE and SOS, a new generation of drilling centre dedicated for hard materials such as titanium, and high thicknesses. The first application was for the assembly of the primary structure of A350 engine pylons. The main solution that was implemented meeting several objectives was the development of orbital drilling technology in hard metal stacks.

ISO 26262 is the actual standard for Functional Safety of automotive E/E (Electric/Electronic) systems. One of the challenges in the application of the standard is the distribution of safety related activities among the participants in the supply chain. In this paper, the concept of a Safety Element out of Context (SEooC) development will be analyzed showing its current problematic aspects and difficulties in implementing such an approach in a concrete typical automotive development flow with different participants (e.g. from OEM, tier 1 to semiconductor supplier) in the supply chain. The discussed aspects focus on the functional safety requirements of generic hardware and software development across the supply chain where the final integration of the developed element is not known at design time and therefore an assumption based mechanism shall be used.

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 A number of studies have shown that driving an unfamiliar vehicle has the potential to introduce additional risk, especially for novice drivers. However, such studies have generally used statistical methods based on analyzing crash and near-crash data from a range of driver groups, and therefore the evaluation has the potential to be subjective and limited. For a more objective perspective, this study suggests that it would be worthwhile to consider vehicle dynamic signals obtained from the Controller Area Network (CAN-Bus) and smartphones. This study, therefore, is focused on the effect of driver experience and vehicle familiarity for issues in driver modeling and distraction. Here, a group of 20 drivers participated in our experiment, with 13 of them having participated again after a one-year time lapse in order for analysis of their change in driving performance.

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 In today’s competitive automobile market, driver comfort is at utmost importance and the bar is being raised continuously. Gear Shifting is a crucial customer touch point. Any issue or inconvenience caused while shifting gear can result into customer dissatisfaction and will impact the brand image. While there are continual efforts being taken by most of the car manufactures, “Double Bump” in gearshift has remained as a pain area and impact severely on the shift feel. This is more prominent in North-South (N-S) transmissions. In this paper ‘Double Bump’ is a focus area and a mathematical / analytical approach is demonstrated by analyzing ‘impacting parameters’ and establishing their co-relation with double bump. Additionally, the results are also verified with a simulation model.

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.

1. On page 111, the authors have described a method to assess driver distraction. In this method, participants maintained a white square size on a forward display by using a game gas pedal of like in car-following situation. The size of the white square is determined by calculating the distance to a virtual lead vehicle. The formulas to correct are used to explain variation of acceleration of the virtual lead vehicle. The authors inadvertently incorporated old formulas they had used previously. In the experiments discussed in the article, the corrected formulas were used. Therefore, there is no change in the results. The following from the article:

Abstract A key element in an ergonomically designed driver’s seat in a car is the correct identification of driver seating position and posture accommodation. Current practice by the automotive Original Equipment Manufacturer (OEM) is to utilize the Society of Automotive Engineering (SAE) J1517 standard practice as a reference. However, it was found that utilizing such guidelines, which were developed based on the American population, did not fit well with the anthropometry and stature of the Malaysian population. This research seeks to address this issue by comparing the SAE J1517 Model against Malaysian preferred driving position. A total of 62 respondents were involved for the driver seating position and accommodation study in the vehicle driver’s seat buck mockup survey and measurements. The results have shown that the Malaysian drivers prefer to sit forward as compared to the SAE J1517 Model and have shorter posture joint angle.

Abstract The advancement towards development of autonomy follows either the bottom-up approach of gradually improving and expanding existing Advanced Driver Assist Systems (ADAS) technology where the driver is present in the control loop or the top-down approach of directly developing autonomous vehicle hardware and software using alternative approaches without the driver present in the control loop. Most ADAS systems today fall under the classification of SAE Level 1 which is also referred to as the driver assistance level. The progression from SAE Level 1 to SAE Level 2 or partial automation involves the critical task of merging automated lateral control and automated longitudinal control such that the tasks of steering and acceleration/deceleration are not required to be handled by the driver under certain conditions [1].

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.