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In this paper, we present an end-to-end real-time detection and collision avoidance framework in an autonomous vehicle using a monocular RGB camera. The proposed system is able to run on embedded hardware in the vehicle to perform real-time detection of small objects. RetinaNet architecture with ResNet50 backbone is used to develop the object detection model using RGB images. A quantized version of the object detection inference model is implemented in the vehicle using NVIDIA Jetson AGX Xavier. A geometric method is used to estimate the distance to the detected object which is forwarded to a MicroAutoBox device that implements the control system of the vehicle and is responsible for maneuvering around the detected objects. The pipeline is implemented on a passenger vehicle and demonstrated in challenging conditions using different obstacles on a predefined set of waypoints.

Prior to developing or modifying the protocol of a performance evaluation test, it is important to identify field relevant conditions. The objective of this study was to assess the distribution of selected crash variables from rear crash field collisions involving modern vehicles. The number of exposed and serious-to-fatally injured non-ejected occupants was determined in 2008+ model year (MY) vehicles using the NASS-CDS and CISS databases. Selected crash variables were assessed for rear crashes, including severity (delta V), impact location, struck vehicle type, and striking objects. In addition, 15 EDRs were collected from 2017 to 2019 CISS cases involving 2008+ MY light vehicles with a rear delta V ranging from 32 to 48 km/h. Ten rear crash tests were also investigated to identify pulse characteristics in rear crashes. The tests included five vehicle-to-vehicle crash tests and five FMVSS 301R barrier tests matching the struck vehicle.

Studies regarding the automotive communication networks has been a fast-growing subject of research through automotive companies. One of the main issues during a vehicle network prototype development is the necessity of a programmed hardware for gateway modules. The gateway developed by an automotive supplier, usually implemented at an Electronic Control Unit (ECU), is one of the last remaining modules to have a software available because it needs to wait the message list completion of all ECUs prior to set up its own message list and network interfaces. This paper proposes a method to design and test Controlled Area Network (CAN) gateways using Vector CANoe software by developing ways to facilitate a scalable code production in CAPL scripts. Use cases will use gatewayed signals virtually validated prior to software deployment, along with codes proposals and common cases examples.

The automotive industry is passing for a big transformation, due to technologies advance. The electrical technologies are also on a good rising curve, calling the attention of the Original Equipment Manufacturer (OEMs). This scenario generates the demand for a faster method to train their new hired engineers, when compared with usual on the job training. Model in the Loop (MiL) consists in one of the real-time embedded systems test phases, which is developed in a computational environment, performing a mathematical modeling of the system, presenting an interface that allows the visualization of its dynamics and the signals involved. Two powerful software in industry that apply MiL are the Matlab and Simulink. A project involving these applications was proposed for a team of new hired engineers, developing models of several vehicle Electronic Control Units (ECUs), with some scope reduction as an example the functional requirements reduction.

This article aims to analyze the potential economic effects for consumers with the implementation of the Vehicle-to-Grid (V2G) and the Vehicle-to-Home (V2H) networks in Brazil. Nowdays, the usage of both technologies in Brazil are at a regulatory vacuum for both Battery Electric Vehicles (BEVs), Plug-in Hybrid Electric Vehicles (PHEVs) and Hybrid Electric Vehicles (HEVs). Usually, when a legislation lack occurs, the local OEMs adopt IECs (International Electrotechnical Commissions) and/or SAEs (Society of Automotive Engineers) reference international standards, such as SAE J1634 for range test.

In this paper, a novel methodology of nonlinear control is used, and a passivity-based control of contractive port-controlled Hamiltonian (PCH) systems is applied to a permanent magnet synchronous motor (PMSM). This methodology, also called “tIDA-PBC” (Trajectory Injection and Damping Assignment—Passivity-Based Control), uses passivity-based control of PCH systems “IDA-PBC” and exploits the properties of contractive Hamiltonian systems, resulting in a closed loop with its contractive system desired dynamics, thus obtaining an exponential trajectory tracking without relying on the error coordinates. In this system, a few steps are proposed in order to divide and modularize the methodology so it can be redesigned or reapplied in other systems by the reader. First, we define the model and set the way to solve the “matching equation.” Then the feasible and reference trajectories are obtained.

Researchers and engineers are utilizing big data analytics to draw further insights into transportation systems. Large amounts of data at the individual vehicle trip level are being collected and stored. The true potential of such data is still to be determined. In this paper, we are presenting a data-driven, novel, and intuitive approach to model driver behaviors using microscopic traffic simulation. Our approach utilizes metaheuristic methods to create an analytical tool to assess vehicle performance. Secondly, we show how microscopic simulation run outputs can be post-processed to obtain vehicle and trip level performance metrics. The methodology will form the basis for a data-driven approach to unearthing trip experiences as realized by drivers in the real world. The methodology will contribute to, A.) Using vehicle trajectory traces to identify underlying vehicle maneuver distributions as obtained from real-world driver data, B.)

Exhaust sensors and actuators used in automotive applications are subjected to wide variety of operating ambient conditions , the performance of these actuators is challenging especially at cold ambient operating conditions, active exhaust tuning valves with position sensors are used to adjust the sound levels, or noise, vibration and harshness (NVH) from a control unit within the vehicle that leads to an improved driving experience wherein the driver selects their preferred sound levels. However, the operating behavior is crucially influenced by the characteristics of the drive cycle and ambient temperature. The study in this paper is intended to evaluate the icing formation at the start of drive cycle and at different ambient temperature conditions. The test data were obtained through real road and chassis dyno testing at different ambient conditions.

A cast magnesium AE44 subframe was designed and manufactured for a C Class sedan to reduce weight and improve vehicle fuel economy. Corrosion mitigation strategies were developed to reduce the likelihood of galvanic corrosion. Both a proving ground vehicle corrosion test and a laboratory component corrosion test were conducted. The vehicle test result demonstrated that the corrosion mitigation strategies were effective. They also provided lessons learned on clearance between magnesium and steel components and options to improve the subframe’s corrosion resistance. The magnesium subframe achieved 5 kg (32%) weight reduction from the equivalent steel subframe and met all the required structural performance targets.

A cast magnesium subframe was designed and manufactured for a C Class sedan to reduce weight and improve vehicle fuel economy. The magnesium subframe achieved 5 kg (32%) weight reduction from the equivalent steel subframe and met all the required structural performance targets. All the joints of the magnesium subframe were tested for bolt load retention. The tests were conducted with a temperature profile of 100°C to -30°C designed to investigate the creep behavior of the selected magnesium alloy AE44 under high stress.

As the world searches for ways to reduce humanity’s impact on the environment, the automotive industry looks to extend the viable use of the gasoline engine by improving efficiency. One way to improve engine efficiency is through more effective control. Torque-based control is critical in modern cars and trucks for traction control, stability control, advanced driver assistance systems, and autonomous vehicle systems. Closed loop torque-based engine control systems require feedback signal(s); indicated mean effective pressure (IMEP) is a useful signal but is costly to measure directly with in-cylinder pressure sensors. Previous work has been done in torque and IMEP estimation using crankshaft acceleration and ion sensors, but these systems lack accuracy in some operating ranges and the ability to estimate cycle-cycle variation.

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source.

The software gradually takes over more and more tasks of the driver and paves the way to autonomous driving. Software development and software verification is therefore crucial for manufacturer's success. Standards such as ISO 26262 highly recommend requirements-based verification. Agile development uses continuous integration testing based on test automation and evaluation. All this pushed the creation of a model-based software verification environment that provides test generation and test automatization for all kinds of signal-based tests along the V-model. This paper presents a novel core component of this environment, which is as far as to the extent possible a standard-compliant cloud-based solution to test automation at the hardware level. Based on characteristic properties of testbenches, such as the wiring or the connected ECUs, hardware resources available at remote locations can be fully automated.

Historical driver behavior and drive style are crucial inputs in addition to V2X connectivity data to predict future events as well as fuel consumption of the vehicle on a trip. A trip is a combination of different maneuvers a driver executes to navigate a route and interact with his/her environment including traffic, geography, topography, and weather. This study leverages big data analytics on real-world customer driving data to develop analytical modeling methodologies and algorithms to extract maneuver-based driving characteristics and generate a corresponding maneuver distribution. The distributions are further segmented by additional categories such as customer group and type of vehicle. These maneuver distributions are used to build an aggressivity distribution database which will serve as the parameter basis for further analysis with traffic simulation models.

The interaction of Connect and Automated vehicles (CAV) with regular vehicles in the traffic stream has been extensively researched. Most studies, however, focus on calibrating driver behavior models for CAVs based on various levels of automation and driver aggressiveness. Other related studies largely focus on the coordination of CAVs and infrastructure like traffic signals to optimize traffic. However, the effects of different strategic flow management of CAVs in the traffic stream in the comparative scenario-based analysis is understudied. Thus, this study develops a framework and simulations for integrating CAVs in a corridor section. We developed a calibrated model with CAVs for a corridor section in Corktown, Michigan, and simulate how dedicated CAV lane operations can be implemented without significant change in existing infrastructure.

Automotive industry is inclined towards connected, comfortable, environment friendly, efficient and smarter systems. Advanced Driver Assist System (ADAS) technology assist drivers to achieve a safer as well as better ride by automation and improvisation of the vehicular systems. With the advent of ADAS system, there is a significant focus not only in the development of Electronic Control Units (ECUs) and its features to cater to the emerging market but also on the information that could be displayed to meet the functional as well as safety requirements. This ADAS information display ensures timely notification to the driver with unique alerts that can be acoustic or visual. These systems should be tested thoroughly to ensure reliability as failures may impose severe risk on the OEM. Hardware in the loop testing has been largely adopted by industry against manual testing in lieu of the testing constraints imposed by the latter.

The work presented here is part of the research done in the field of voice biometrics. This paper helps to understand the state-of-the-art in speaker recognition technology potentially capable of solving challenges related to speaker identification (to identify a speaker among multiple speakers) and speaker verification/authentication (to recognize the current speaking person at a pre-defined access level and authenticate accordingly). The research was focused on performing an unbiased evaluation of two individual voice biometric services. The level of accuracy in identifying and authenticating individuals using these services provides an insight into the current state of technology and the state of what other dual authentication methods could be used to achieve a desired True Acceptance Rate (TAR) and False Acceptance Rates (FAR).

Variant modeling techniques have been developed to allow systems engineers to model multiple similar variants in a product line as a single variant model. In this paper, we expand on this past work to explore the extent to which variant modeling in SysML can be applied to a broad range of dissimilar systems, covering the entire domain of ground vehicles, in single reference architecture model. Traditionally, a system’s structure is decomposed into subsystems and components. However, this method is found to be ineffective when modeling variants that are functionally similar but structurally different. We propose to address this challenge by first decomposing the system not only by subsystem but also by high-level function. This pattern is particularly useful for situations where two variants perform the same function, but one variant performs the function using one subsystem, whereas the other variant performs the same function using one or more different subsystems.

Fuel power consumption for light duty vehicles has previously been shown to be proportional to vehicle traction power, with an offset for overhead and accessory losses. This allows the fuel consumption for an individual powertrain to be projected across different vehicles, missions, and drive cycles. This work applies the power-based model to commercial vehicles and demonstrates its usefulness for projecting fuel consumption on both regulatory and customer use cycles. The ability to project fuel consumption to different missions is particularly useful for commercial vehicles, as they are used in a wide range of applications and with customized designs. Specific cases are investigated for Light and Medium Heavy- Duty work trucks. The average power required by a vehicle to drive the regulatory cycles varies by nearly a factor 10 between the Class 4 vehicle on the ARB Transient cycle and the loaded Class 7 vehicle at 65 mph on grade.

Electrical connectors and terminals are widely used in the automotive industry. It is desirable to mate the electrical connections using materials or coatings with low friction force to improve the ergonomics of the assembly process while maintaining good electrical conduction over the lifetime of the vehicle. We have previously shown that plasma-enhanced chemical vapor deposition (PECVD) of graphene on gold (Au) and silver (Ag) terminals can significantly reduce the insertion force (friction force during the terminal insertion process). However, the cost of this deposition method is rather high, and its high temperature process (> 400 oC) makes it impractical for materials with low melting temperatures. For example, tin (Sn) coating with a melting temperature of 232 oC is commonly used in electrical connectors, which cannot sustain the high temperature process. In this study, reduced graphene oxide was prepared using a low-cost solution process and applied onto metallic terminals.