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Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

As the new features for driver assistance and active safety systems are growing rapidly in vehicles, the simulation within a virtual environment has become a necessity. The current active safety system consists of Electronic Control Units (ECUs) which are coupled to camera and radar sensors. Two methods of implementation exists, integrated sensors with control modules or separation of sensors form control modules. The subsystem integration testing poses new challenges for virtual environment for simulation of active safety features. The comprehensive simulation environment for integration testing consists of chassis controls, powertrain, driver assistance, body and displays controllers. Additional complexity in the system is the serial communication strategy. Multiple communication protocols such as GMLAN, LIN, standard CAN, and Flexray could be present within the same vehicle topology.

The USDOT and the Crash Avoidance Metrics Partnership-Vehicle Safety Communications 2 (CAMP-VSC2) Consortium (Ford, GM, Honda, Mercedes, and Toyota) initiated, in December 2006, a three-year collaborative effort in the area of wireless-based safety applications under the Vehicle Safety Communications-Applications (VSC-A) Project. The VSC-A Project developed and tested communications-based vehicle safety systems to determine if Dedicated Short Range Communications (DSRC) at 5.9 GHz, in combination with vehicle positioning, would improve upon autonomous vehicle-based safety systems and/or enable new communications-based safety applications.

An analysis of the first 35 back-over crashes reported by NHTSA's Special Crash Investigations unit was undertaken with two objectives: (1) to test a hypothesized classification of backing crashes into types, and (2) to characterize scenario-specific conditions that may drive countermeasure development requirements and/or objective test development requirements. Backing crash cases were sorted by type, and then analyzed in terms of key features. Subsequent modeling of these SCI cases was done using an adaptation of the Driving Reliability and Error Analysis Methodology (DREAM) and Cognitive Reliability and Error Analysis Methodology (CREAM) (similar to previous applications, for instance, by Ljung and Sandin to lane departure crashes [10]), which is felt to provide a useful tool for crash avoidance technology development.

The United States Department of Transportation (USDOT) and the Crash Avoidance Metrics Partnership-Vehicle Safety Communications 2 (CAMP-VSC2) Consortium (Ford, General Motors, Honda, Mercedes-Benz, and Toyota) initiated, in December 2006, a three-year collaborative effort in the area of wireless-based safety applications under the Vehicle Safety Communications-Applications (VSC-A) Project. The VSC-A Project developed and tested Vehicle-to-Vehicle (V2V) communications-based safety systems to determine if Dedicated Short Range Communications (DSRC) at 5.9 GHz, in combination with vehicle positioning, would improve upon autonomous vehicle-based safety systems and/or enable new communications-based safety applications.

Forward Collision Alert (or Forward Collision Warning) systems provide alerts intended to assist drivers in avoiding or mitigating the harm caused by rear-end crashes. These systems currently use front-grille mounted, forward-looking radar devices as the primary sensor. In contrast, Lane Departure Warning (LDW) systems employ forward-looking cameras mounted behind the windshield to monitor lane markings ahead and warn drivers of unintended lane violations. The increasing imaging sensor resolution and processing capability of forward-looking cameras, as well recent important advances in machine vision algorithms, have pushed the state-of-the-art for camera-based features. Consequently, camera-based systems are emerging as a key crash avoidance system component in both a primary and supporting sensing role. There are currently no production vehicles with cameras used as the sole FCA sensing device.

Vehicle-to-vehicle (V2V) communication systems can enable a number of wireless-based vehicle features that can improve traffic safety, driver convenience, and roadway efficiency and facilitate many types of in-vehicle services. These systems have an extended communication range that can provide drivers with information about the position and movements of nearby V2Vequipped vehicles. Using this technology, these vehicles are able to communicate roadway events that are beyond the driver's view and provide advisory information that will aid drivers in avoiding collisions or congestion ahead. Given a typical communication range of 300 meters, drivers can potentially receive information well in advance of their arrival to a particular location. The timing and nature of presenting V2V information to the driver will vary depending on the nature and criticality of the scenario.

Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are gaining increasing importance in automotive research and engineering domains. The novel communication scheme is targeted to improve driver safety (e.g., forward collision warnings) and comfort (e.g., routing to avoid congestion, automatic toll collection, etc.). Features exploiting these communication schemes are still in the early stages of research and development. However, growing attention to system wide infrastructure - in terms of OEM collaboration on interface standardization, protocol standardization, and government supported road/wireless infrastructure - will lead to popularity of such features in the future. This paper focuses on evaluating reliability and safety/integrity of data communicated over the wireless channels for early design verification. Analysis of a design can be done based on formal models, simulation, emulation, and testing.

Fuel economy improvement and stringent emission regulations worldwide require advanced air charging and combustion technologies, such as low temperature combustion, PCCI or HCCI combustion. Furthermore, NOx aftertreatment systems, like Selective Catalyst Reduction (SCR) or lean NOx trap (LNT), are needed to reduce vehicle tailpipe emissions. The information on engine-out NOx emissions is essential for engine combustion optimization, for engine and aftertreatment system development, especially for those involving combustion optimization, system integration, control strategies, and for on-board diagnosis (OBD). A physical NOx sensor involves additional cost and requires on-board diagnostic algorithms to monitor the performance of the NOx sensor.

This paper discusses observability of the vehicle states using different sensor configurations as well as fault-tolerant estimation of these states. The optimality of the sensor configurations is assessed through different observability measures and by using a 3-DOF linear vehicle model that incorporates yaw, roll and lateral motions of the vehicle. The most optimal sensor configuration is adopted and an observer is designed to estimate the states of the vehicle handling dynamics. Robustness of the observer against sensor failure is investigated. A fault-tolerant adaptive estimation algorithm is developed to mitigate any possible faults arising from the sensor failures. Effectiveness of the proposed fault-tolerant estimation scheme is demonstrated through numerical analysis and CarSim simulation.

Remote vehicle start systems are commonly available as an aftermarket accessory, and more recently, as a factory installed vehicle feature. These systems and their associated algorithms enable a user of the vehicle to remotely start the engine and/or other vehicle systems with the end goal of preconditioning the cabin environment, for example, if the user wishes to have the vehicle's interior heated or cooled before the user enters the vehicle. However, if the engine is remotely started for an extended period of time, the increased use of fuel, energy, and/or other resources may be greater than optimal or desired. Through the use of available vehicle sensors and enhanced algorithms, a system can be implemented which allows the passenger cabin to be heated or cooled to within a range of moderate temperatures, while reducing the resources utilized by the vehicle.

Key fobs, also known as remote keys or remote transmitters, have become a common piece of equipment in today's vehicle, being ubiquitous in every market segment. Once limited to remote locking and unlocking operations, today's key fobs can be used to control many comfort and security features beyond locking and unlocking, such as alarm system operation, vehicle locate, approach lighting, memory seat recall, and remote starting systems. Key fobs are designed to be easy to use as well as easy to carry and transport in personal containers, such as purses, pockets, wallets, and the like. Accordingly, as with other personal effects, key fobs and other portable remote devices can be lost or misplaced or can be otherwise difficult to find. Even with careful tracking of a remote device, children and pets, among other factors, can make location difficult. Moreover, multiple remote devices are often distributed with each vehicle.

This paper presents Telematics Battery Monitoring (TBM). TBM is a multi-faceted approach of collecting and analyzing electric power and vehicle data used to ultimately determine battery state of charge (SOC) and battery state of health (SOH) in both pre- and post-sale environments. Traditional methods of battery SOC analysis include labor intensive processes such as going out to the site of individual vehicle(s), gaining access to the vehicle battery, and then after the vehicle electrical system obtains its quiescent current level, performing a battery voltage check. This time-consuming manual method can practically only cover a small percentage of the vehicle population. In using the vehicle communication capabilities of Telematics, electric power and vehicle data are downloaded, compiled, and post-processed using decision-making software tools.

An accurate estimation of cycle-by-cycle in-cylinder mass and the composition of the cylinder charge is required for spark-ignition engine transient control strategies to obtain required torque, Air-Fuel-Ratio (AFR) and meet engine pollution regulations. Mass Air Flow (MAF) and Manifold Absolute Pressure (MAP) sensors have been utilized in different control strategies to achieve these targets; however, these sensors have response delay in transients. As an alternative to air flow metering, in-cylinder pressure sensors can be utilized to directly measure cylinder pressure, based on which, the amount of air charge can be estimated without the requirement to model the dynamics of the manifold.

The increased market share of electric vehicles and renewable energy resources have raised concerns about their impact on the current electrical distribution grid. To achieve sustainable and stable power distribution, a lot of effort has been made to implement smart grids. This paper addresses Demand Response (DR) load control in a smart grid using Internet of Things (IoT) technology. A smart grid is a networked electrical grid which includes a variety of components and sub-systems, including renewable energy resources, controllable loads, smart meters, and automation devices. An IoT approach is a good fit for the control and energy management of smart grids. Although there are various commercial systems available for smart grid control, the systems based on open sources are limited. In this study, we adopt an open source development platform named Node-RED to integrate DR capabilities in a smart grid for DR load control. The DR system employs the OpenADR standard.

The engineering of electric propulsion systems requires time and cost efficient methodologies to determine system characteristics as well as potential component integration issues. A significant part of this analysis is the identification of the electromagnetic fields present in the propulsion system. Understanding of the electromagnetic fields during system operation is a significant design consideration due to the use of components that require large current(s) and high voltage(s) in the proximity of other control system items (such as sensors) that operate with low current(s) and voltage(s). Therefore, it is critical to quantify the electromagnetic fields produced by these components within the design and how they may interact with other system components. Often overlooked (and also extremely important) is an evaluation of how the overall system architecture can generate or react to electromagnetic fields (which may be a direct result of packaging approaches).

Recent trends in the automotive industry show growing demands for the introduction of new in-vehicle features (e.g., smart-phone integration, adaptive cruise control, etc.) at increasing rates and with reduced time-to-market. New technological developments (e.g., in-vehicle Ethernet, multi-core technologies, AUTOSAR standardized software architectures, smart video and radar sensors, etc.) provide opportunities as well as challenges to automotive designers for introducing and implementing new features at lower costs, and with increased safety and security. As a result, the design of Electrical/Electronic (E/E) architectures is becoming increasingly challenging as several hardware resources are needed. In our earlier work, we have provided top-level definitions for three relevant metrics that can be used to evaluate E/E architecture alternatives in the early stages of the design process: flexibility, scalability and expandability.

In-vehicle electronics is displacing the traditional mechanical interfaces and as a result, electrical architecture design is evolving and getting more complex due to increase in automotive electronic content. Several embedded communication protocols are used to build an electrical architecture, with predominant use of Controlled Area Network (CAN) and Local Interconnection Network (LIN). Demand for new electrical features is increasing, to meet and to exceed the customer expectations and also to adapt to new evolving electronic technologies. To accommodate future electrical content, the need for communication bandwidth is increasing at an exponential rate. In addition, some of the safety-critical features require predictability and deterministic network behavior. Current protocols are not capable of satisfying these demands. FlexRay protocol can address these needs with higher bandwidth and determinism.

This paper describes how information available through the OnStar system represents a unique and powerful mechanism to assess field crash rates. Included within is a description of how vehicle and OnStar information may be gathered, organized and analyzed. The resulting data provides the capability to conducts various studies of field activity and/or events. In this case, a study was conducted to try to determine if certain vehicle equipment might have an impact on field crash rates. The process is exemplified via a description of a study conducted by GM OnStar in 2009. Two analyses were conducted comparing crash rates of selected vehicle models, with and without certain advanced safety sensing and warning features. Specifically, beginning in the 2008 Model Year, General Motors introduced Lane Departure Warning and Side Blind Zone Alert into US/Canada production. Utilizing data on crashes, drawn from OnStar Automatic Crash Response events, analyses of crash rates were conducted.

This paper is the second in the series of documents designed to record the progress of a series of SAE documents - SAE J2836™, J2847, J2931, & J2953 - within the Plug-In Electric Vehicle (PEV) Communication Task Force. This follows the initial paper number 2010-01-0837, and continues with the test and modeling of the various PLC types for utility programs described in J2836/1™ & J2847/1. This also extends the communication to an off-board charger, described in J2836/2™ & J2847/2 and includes reverse energy flow described in J2836/3™ and J2847/3. The initial versions of J2836/1™ and J2847/1 were published early 2010. J2847/1 has now been re-opened to include updates from comments from the National Institute of Standards Technology (NIST) Smart Grid Interoperability Panel (SGIP), Smart Grid Architectural Committee (SGAC) and Cyber Security Working Group committee (SCWG).