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Advanced Driver Assist System (ADAS) and autonomous vehicle technologies have disrupted the traditional automotive industry with potential to increase safety and optimize the cost of car ownership. Among the challenges are those of sensing the environment in and around the vehicle. Infrared camera sensing is seeing a rapid growth and adoption in the industry. The applications and illumination architecture options continue to evolve. This course will provide the foundation on which to build near infrared camera technologies for automotive applications.

课程概述 Powertrain controls for NEVs are one of the most complex and highly confidential areas of NEV research and development.  This two-day course takes the seemingly complicated field of NEV powertrain controls and summarizes it into a few basic principles.  The latest and most popular NEV powertrains are also reviewed to illustrate these principles and the controls strategies used.  对于新能源汽车来说，动力总成控制一直以来都是最复杂的和高度机密的领域之一。在这两天的课程中，我们将把看似复杂的动力总成控制系统总结出几条基本规则，同时，通过对当今其他车型动力控制系统的案例分析，来把这些规则和原理进行融会贯通。

Convolutional neural networks are the de facto method of processing camera, radar, and lidar data for use in perception in ADAS and L4 vehicles, yet their operation is a black box to many engineers. Unlike traditional rules-based approaches to coding intelligent systems, networks are trained and the internal structure created during the training process is too complex to be understood by humans, yet in operation networks are able to classify objects of interest at error rates better than rates achieved by humans viewing the same input data.

This 4-week virtual-only experience is conducted by leading experts in the autonomous vehicle industry and academia. You’ll develop an understanding of the fundamentals of AV architecture, including mechatronics, kinematics, and the sense-think-act framework in autonomous systems. The course builds a connection for how robotics are used in autonomous vehicles and provides you with demonstrations, procedures, and the skills necessary to program a robot with basic commands using the Robot Operating System (ROS).

Many technical projects, most vehicle and component testing, and all accident reconstructions, product failure analyses, and other forensic investigations, require photographic documentation. Roadway evidence disappears, tested or wrecked vehicles are repaired, disassembled, or scrapped, and components can be tested for failure. Photographs are frequently the only evidence that remains of a wreck, or the only records of subjects before or during tests. Making consistently good images during any inspection is a critical part of the evaluation process. 

This article characterizes the special features of drilling of CFRP/Titanium and -Aluminium stacks. Simplified theoretic models will show how CFRP/Titanium stacks should be machined without scratches and burn marks contacting carbon. Low axial forces and smart chip removal technology are the main characteristics of the drilling tool technology, optimized to reach H8 quality in one shot operation. Presenter Peter Mueller-Hummel, Cutting Tools Inc.

In any new aircraft development program there are many important design decisions that determine profitability potential. The key to making new aircraft profitable is to design features that will command more money than the cost to provide them within the market's ability to absorb them. The business model in this paper shows how to predict or find: 1) the costs to provide various aircraft features; 2) the values that aircraft buyers place on these features; 3) the amount of money that buyers have to commit to them, 4) the open spaces in the market in which to place new designs and 5) the predicted profits from new designs. In this process, this paper extends previous work on the law of value and demand, which states that attributes determine value; value determines price; and that price determines demand. This four-dimensional, non-negative system hosts a business model that describes the features needed to enable aircraft designs to go from concepts to profitable assembly lines.

The presentation describes technology developments and the integration of these technologies into new emission control systems. As in other years, the reader will find a wide range of topics from various parts of the world. This is reflective of the worldwide scope and effort to reduce diesel exhaust emissions. Topics include the integration of various diesel particulate matter (PM) and Nitrogen Oxide (NOx) technologies as well as sensors and other emissions related developments. Presenter Atsuo Kondo, NGK Insulators, Ltd.

For internal combustion engines and industrial machinery, it is well recognized that the most cost-effective way of reducing energy consumption and extending service life is through lubricant development. This presentation summarizes our recent R&D achievements on developing a new class of candidate lubricants or oil additives ionic liquids (ILs). Features of ILs making them attractive for lubrication include high thermal stability, low vapor pressure, non-flammability, and intrinsic high polarity. When used as neat lubricants, selected ILs demonstrated lower friction under elastohydrodynamic lubrication and less wear at boundary lubrication benchmarked against fully-formulated engine oils in our bench tests. More encouragingly, a group of non-corrosive, oil-miscible ILs has recently been developed and demonstrated multiple additive functionalities including anti-wear and friction modifier when blended into hydrocarbon base oils.

This presentation will introduce the overall goals of the EcoCAR competition in brief, and will go into the third and final year of the competition in detail. The final year of competition saw teams refining and testing their student-built advanced technology vehicles including hybrids, plug-in hybrids, hydrogen fuel cell PHEVs and one battery electric. Important events, such as the Spring Workshop chassis dynamometer testing event at the U.S. Environmental Protection agency, as well as significant competition results, such as vehicle performance, consumer acceptability and efficiency will be presented. Presenter Patrick Walsh

Simulation-based tolerance analysis is the accepted standard for dimensional engineering in aerospace today. Sophisticated 3D model-based tolerance analysis processes enable engineers to measure variation in complex, often large, assembled products quickly and accurately. Best-in-class manufacturers have adopted Quality Intelligence Management tools for collecting and consolidating this measurement data. Their goal is to completely understand dimensional fit characteristics and quality status before commencing the build process. This results in shorter launch cycles, improved process capabilities, reduced scrap and less production downtime. This paper describes how to use simulation-based approaches to correlate the theoretical tolerance analysis results produced during engineering simulations to actual as-built results. This allows engineers to validate or adjust as-designed simulation parameters to more closely align to production process capabilities.

The foundation of many production aircraft assembly facilities is a more dynamic and unpredictable quantity than we would sometimes care to admit. Any tooling structures constructed on these floors, no matter how thoroughly analyzed or well understood, are at the mercy of settling and shifting concrete, which can cause very lengthy and costly periodic re-certification and adjustment procedures. It is with this in mind, then, that we explore the design possibilities for one such structure to be built in Belfast, North Ireland for the assembly of the Shorts C-Series aircraft wings. We evaluate the peak floor pressure, weight, gravity deflection, drilling deflection, and thermal deflection of four promising structures and discover that carefully designed pivot points and tension members can offer significant benefits in some areas.

Impact of driving patterns on fuel economy is significant in hybrid electric vehicles (HEVs). Driving patterns affect propulsion and braking power requirement of vehicles, and they play an essential role in HEV design and control optimization. Driving pattern conscious adaptive strategy can lead to further fuel economy improvement under real-world driving. This paper proposes a real-time driving pattern recognition algorithm for supervisory control under real-world conditions. The proposed algorithm uses reference real-world driving patterns parameterized from a set of representative driving cycles. The reference cycle set consists of five synthetic representative cycles following the real-world driving distance distribution in the US Midwestern region. Then, statistical approaches are used to develop pattern recognition algorithm. Driving patterns are characterized with four parameters evaluated from the driving cycle velocity profiles.

Using pressure sensitive adhesive tapes to achieve economy and efficiency in automotive assembly

Exatec� PC glazing technology team, has developed advanced weathering and abrasion resistant coatings technology that can be applied to protect polycarbonate. It is of particular interest to quantify and understand the factors that determine the surface abrasion performance of coated PC in rear window and backlight applications that have a wiper system. In the present study we describe Exatec's lab scale wiper testing equipment and test protocols. We also describe adaptation of optical imaging system to measure contrast and nano-profiling using nano-indenter, as post wiper surface characterization methods. These methods are more sensitive to fine scratches on glazing surface than standard haze measurement and mechanical profilometry. Three coating systems were investigated; Siloxane wetcoat (A), Siloxane wetcoat (B), and Siloxane wetcoat (B) plus plasma coat (Exatec� E900 coating). The performance comparisons were made using all these surface characterization methods.

Four-way, integrated, diesel emission control systems that combine selective catalytic reduction for NOx control with a continuously regenerating trap to remove diesel particulate matter were evaluated under real-world, on-road conditions. Tests were conducted using a semi-tractor with an emissions year 2000, 6-cylinder, 12 L, Volvo engine rated at 287 kW at 1800 rpm and 1964 N-m. The emission control system was certified for retrofit application on-highway trucks, model years 1994 through 2002, with 4-stroke, 186-373 kW (250-500 hp) heavy-duty diesel engines without exhaust gas recirculation. The evaluations were unique because the mobile laboratory platform enabled evaluation under real-world exhaust plume dilution conditions as opposed to laboratory dilution conditions. Real-time plume measurements for NOx, particle number concentration and size distribution were made and emission control performance was evaluated on-road.

Abstract Gasoline particulate filters (GPFs) are important aftertreatment components that enable gasoline direct injection (GDI) engines to meet European Union (EU) 6 and China 6 particulate number emissions regulations for nonvolatile particles greater than 23 nm in diameter. GPFs are rapidly becoming an integral part of the modern GDI aftertreatment system. The Active Exhaust Tuning (EXTUN) Valve is a butterfly valve placed in the tailpipe of an exhaust system that can be electronically positioned to control exhaust noise levels (decibels) under various vehicle operating conditions. This device is positioned downstream of the GPF, and variations in the tuning valve position can impact exhaust backpressures, making it difficult to monitor soot/ash accumulation or detect damage/removal of the GPF substrate. The purpose of this work is to present a unique example of subsystem control and diagnostic architecture for an exhaust system combining GPF and EXTUN.

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 In this paper, an efficient lane detection using deep feature extraction method is proposed to achieve real-time lane detection in diverse road environment. The method contains three main stages: 1) pre-processing, 2) deep lane feature extraction and 3) lane fitting. In pre-processing stage, the inverse perspective mapping (IPM) is used to obtain a bird's eye view of the road image, and then an edge image is generated using the canny operator. In deep lane feature extraction stage, an advanced lane extraction method is proposed. Firstly, line segment detector (LSD) is applied to achieve the fast line segment detection in the IPM image. After that, a proposed adaptive lane clustering algorithm is employed to gather the adjacent line segments generated by the LSD method. Finally, a proposed local gray value maximum cascaded spatial correlation filter (GMSF) algorithm is used to extract the target lane lines among the multiple lines.

Abstract A torque converter was instrumented with 29 pressure transducers inside five cavities under study (impeller, turbine, stator, clutch cavity between the pressure plate and the turbine shell). A computer model was created to establish correlation with measured torque and pressure. Torque errors between test and simulation were within 5% and K-Factor and torque ratio errors within 2%. Turbulence intensity on the computer model was used to simulate test conditions representing transmission low and high line pressure settings. When turbulence intensity was set to 5%, pressure simulation root mean square errors were within 11%-15% for the high line pressure setting and up to 34% for low line pressure setting. When turbulence intensity was increased to 50% for the low line pressure settings, a 6% reduced root mean square error in the pressure simulations was seen.