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The ISO 26262 series of standards for vehicle functional safety codify requirements to avoid unreasonable risk from the failure of electrical or electronic (E/E) systems. E/E failures may cause malfunctioning behavior that manifest as vehicle-level hazardous events. The ISO 26262 second edition includes commercial trucking, which employs significant variation from the passenger car development cycle. The highly distributed nature of E/E system development and integration in commercial trucks complicates forging unified safety concepts. For instance, the Fault Tolerant Time Interval (FTTI) quantifies the minimum time span from the occurrence of a fault to the possible occurrence of a hazardous event. Often, the subjectivity involved in defining unreasonable risk and hazardous event onset frustrates consensus among stakeholders.

Forty-six successful Six Sigma projects were analyzed in details, aiming to recognize a pattern where some fundamental projects were solved with solutions that were repeatedly used from one project to another to approach problems related to squeaks and rattles (S&R). Quality indicators such as external indicators Things Gone Wrong (CTQ), customer satisfaction (CTS) and internal indicators like warranty spend and rework (CTC) are highly impacted due to S&R mainly because of South America road conditions and driver habits. Ultimate advantages of this paper is to compare problem solving approaches for squeaks and rattles, searching for time efficiency, low cost quality improvement and out-of-box solutions.

Six Sigma initiatives impact customer satisfaction, market competitiveness and profitability. Innovative solutions to effective problem solving and problem prevention are critical elements of any Six Sigma and Design for Six Sigma project. In contrast to well-known idea generation methods such as brainstorming, TRIZ, a Russian acronym for the Theory of Inventive Problem Solving, provides a more structured, better organized, highly repeatable and documented approach to innovative solutions development. Principles, benefits and an industrial case study of TRIZ are discussed in this paper.

There has been a significant increase - both in the content of electronics and software in vehicles as well as in recalls attributed to these components and systems. The advanced features, including the onset of autonomous vehicles accompanied by millions of lines of code in software have exponentially increased the complexity of vehicle systems and decreased effectiveness of many of the safety analysis techniques being used to identify hazards and safety requirements - for example, FMEA, FTA, ETA, etc.- which were invented decades before the existence of complexities of such magnitude. This paper examines a new hazard identification technique formalized by Nancy G Leveson of Massachusetts Institute of Technology (MIT), USA in her book “Engineering a Safer World” and further elaborated in the STPA Handbook co-authored with John P Thomas in March 2018.

Android has gained undisputed leadership in the Smartphone world. It is an open-source framework which enables the community of applications developers to collaborate on a single implementation of a shared product. This collaboration has led to a large applications ecosystem that is unmatched by any other product or device in the world today. Making the Android suitable for the Automotive world brings shortened development time for infotainment features. To do so, it implies combining automotive software libraries, 3rd party applications and native Android applications while coordinating with HMI specialists, under the responsibility of a strong system integrator.

Allison Transmission has developed a family of three new automatic transmission products, the 1000, 2000 and 2400 Series™, for the light and medium-duty commercial markets. This technical paper will discuss how Allison first identified the opportunity, established the market requirements, designed, and validated these new products.

Digital mapping tools have become indispensable for road navigation. Applications like Waze and Google Maps harness the power of satellite imagery to provide precise visualization of GPS coordinates. The field advanced significantly in May 2023 with the introduction of dynamic 3D representations of the Earth. Companies such as Cesium now offer Unity3D and Unreal Engine Application Programming Interface that can be applied to geospatial applications. These images are no longer static and offer the opportunity to provide seamless continuous navigation. Driving simulation has been widely used for training and research. We investigate with this project the potential of this new geospatial database as a tool for scenario development to study manual and autonomous driving. We present an in-vehicle driving simulation integration that employs a real steering wheel and pedals from a stationary vehicle as controls.

Lexan 500 is a new polycarbonate grade that was developed to meet market needs for a material combining the strength and durability of metals with lighter weight, design flexibilities, and processing economics of plastics. Lexan 500 has a high impact strength, an excellent dimensional stability, a low coefficient of thermal expansion, and a low water absorption rate. Lexan 500 can be used as a metal replacement in areas where designers can take advantage of the benefits of a structurally rigid thermoplastic without sacrificing impact strength, with added advantages of reduced processing costs and elimination of costly secondary finishing and assembly operations. Lexan 500 is particularly suited to applications requiring high dimensional stability and the ability to mold easily to close tolerances while maintaining excellent surface appearance.

This paper outlines the characterization of a Li-Ion Iron Phosphate battery pack with nominal voltage of 700V as well as the modeling of this pack as an equivalent electrical circuit (EEC) for the purpose of vehicle simulations. For a higher level of fidelity and accuracy, the equivalent circuit is initially modeled as an R-2RC circuit which consists of a voltage source with one resistor (R) and two resistor-capacitor (RC) branches. In this modeling effort, first, several open circuit voltage (OCV) determination methods in the literature are benchmarked and state-of-charge (SOC) dependent OCV curve which is used in the voltage source of the EEC model is derived. Then, two methods of parameter estimation of the EEC are developed for both step current and dynamic current profiles. The first estimation method is applicable to discharge or charge step currents and relies mostly on the relaxation portion of the battery response and involves some manual calibration.

Due to their high energy density, power density, and durability, lithium-ion (Li-ion) batteries are rapidly becoming the most popular energy storage method for electric vehicles. Difficulty arises in accurately estimating the amount of left capacity in the battery during operation time, commonly known as battery state of charge (SOC). This paper presents a comparative study between six different Equivalent Circuit Li-ion battery models and two different state of charge (SOC) estimation strategies. The Battery models cover the state-of-the-art of Equivalent Circuit models discussed in literature. The Li-ion battery SOC is estimated using non-linear estimation strategies i.e. Extended Kalman filter (EKF) and the Smooth Variable Structure Filter (SVSF). The models and the state of charge estimation strategies are compared against simulation data obtained from AVL CRUISE software.

Hybrid, plug-in hybrid, and electric vehicles have enthusiastically embraced rechargeable Li-ion batteries as their primary/supplemental power source of choice. Because the state of charge (SoC) of a battery indicates available remaining energy, the battery management system of these vehicles must estimate the SoC accurately. To estimate the SoC of Li-ion batteries, we derive a normalized state-space model based on Li-ion electrochemistry and apply a Bayesian algorithm. The Bayesian algorithm is obtained by modifying Potter's squareroot filter and named the Potter SoC tracker (PST) in this paper. We test the PST in challenging test cases including high-rate charge/discharge cycles with outlier cell voltage measurements. The simulation results reveal that the PST can estimate the SoC with accuracy above 95% without experiencing divergence.

This paper is a summation of past, present and future work with Li-Ion cells. Direct emphasis will be placed upon the development of cycle life and related components. Other discussion includes: cell design, operational temperature variations, rate capability and energy density. Accelerated performance will be characterized both in GEO and LEO “on-orbit” profiles. To support findings and authenticity of the test data being presented, details of the test configuration and test parameters will be covered as applicable. In addition, future endeavors will be discussed, encompassing expected performance improvements and developmental planning. The intent is to keep the satellite community abreast of recent developments at the cell level and to help direct sound decisions in power system design variations from the current Nickel-Hydrogen (NiH2) and Nickel-Cadmium (NiCd) technologies.

The performance of a High Voltage (HV) battery in an advanced propulsion application is often highly dependent on the customer controlled climate settings of the vehicle system. Cooling of the battery requires an understanding of the environment in which it is expected to operate. Results from testing on various air-cooled systems will be discussed to portray the interactions of the battery system design and the climate control system design. The following topics (in relation to battery cooling) will be discussed: climate control system temperature settings; climate-control system fan settings; climate control system recirculation mode and outside air mode settings; venting of the battery thermal system exhaust air; and the battery location and mounting.

Li-alloy/FeS battery designs, based upon a well-characterized 300-Ah cell developed by Westinghouse Oceanic Division, have been developed for four electric vans currently under development by the U.S. Department of Energy and the Electric Power Research Institute. Computerized cell models were developed to calculate power, energy, weight, and volume values for a cell while varying key design parameters. Battery specifications and vehicle performance are given for the Chrysler TE Van, GMC G-Van, Ford ETX-II, and the Eaton DSEP.

People through many years of experience, have developed a great intuitive sense for navigation and spatial awareness. With this intuition people are able to apply a near rules based approach to their driving. With a transition to autonomous driving, these intuitive skills need to be taught to the system which makes perception is the most fundamental and critical task. One of the major challenges for autonomous vehicles is accurately knowing the position of the vehicle relative to the world frame. Currently, this is achieved by utilizing expensive sensors such as a differential GPS which provides centimeter accuracy, or by using computationally taxing algorithms to attempt to match live input data from LiDARs or cameras to previously recorded data or maps. Within this paper an algorithm and accompanying hardware stack is proposed to reduce the computational load on the localization of the robot relative to a prior map.

In the world of automated driving, sensing accuracy is of the utmost importance, and proving that your sensors can do the job is serious business. This is where ground-truth labeling has an important role in Autoliv’s validation process. Currently, annotating ground-truth data is a tedious and manual effort, involving finding the important events of interest and using the human eye to determine objects from LiDAR point cloud images. We present a workflow we developed in MATLAB to alleviate some of the pains associated with labeling point cloud data from a LiDAR sensor and the advantages that the workflow provides to the labeler. We discuss the capabilities of a tool we developed to assist users in visualizing, navigating, and annotating objects in point cloud data, tracking these objects through time over multiple frames, and then using the labeled data for developing machine learning based classifiers.

Recent developments in the area of autonomous vehicle navigation have emphasized algorithm development for the characterization of LiDAR 3D point-cloud data. The LiDAR sensor data provides a detailed understanding of the environment surrounding the vehicle for safe navigation. However, LiDAR point cloud datasets need point-level labels which require a significant amount of annotation effort. We present a framework which generates simulated labeled point cloud data. The simulated LiDAR data was generated by a physics-based platform, the Mississippi State University Autonomous Vehicle Simulator (MAVS). In this work, we use the simulation framework and labeled LiDAR data to develop and test algorithms for autonomous ground vehicle off-road navigation. The MAVS framework generates 3D point clouds for off-road environments that include trails and trees.

This paper presents a proof of concept for an algorithm to determine the attitude of a multi-layer laser rangefinder or LiDAR (Light Detection And Ranging) relative to a reference frame given the ability of the LiDAR to make measurements to a planar surface with sufficient and proper excitation. Due to the highly non-linear nature of the problem presented, weighted recursive least squares along with a high order unscented transform are used to obtain a final result which is capable of being extended to vehicle safety systems to provide a measurement of the roll and pitch of a vehicle relative to the road's surface with a high degree of accuracy.

LiDAR sensors have played more and more important role on Intelligent and Connected Vehicles (ICV) and Advanced Driver Assistance Systems (ADAS) .However, the development and testing of LiDAR sensors under real driving environment for ADAS applications are greatly limited by various factors, and often are impossible due to safety concerns. This paper proposed a novel functional LiDAR model under virtual driving environment to support development of LiDAR-based ADAS applications under early stage. Unlike traditional approaches on LiDAR sensor modeling, the proposed method includes both geometrical modeling approach and physical modeling approach. While geometric model mainly produces ideal scanning results based on computer graphics, the physical model further brings physical influences on top of the geometric model. The range detection is derived and optimized based on its physical detection and measurement mechanism.

Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines.