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Electronic control units (ECUs) offer a modular, networked approach to real time machine control and diagnostics. Software embedded in these controllers offer agile and customizable solutions because of the intimate relationship with the ECU hardware and its inputs/outputs. In an idealistic view, embedded software should support the machine's life - 30 years or longer. Developing and maintaining software for these systems requires a strategy. A framework demonstrating common building blocks and long-term centralized support for ECUs on a machine is presented. This strategy reduces the detailed knowledge of the specific machine controls needed by ECU developers and provides the components and infrastructure key to extending the life and functionality of the ECU.

The electric power steering (EPS) is increasing its number since there are many advantages compared to hydraulic power steering. The EPS saves fuel and eliminates hydraulic fluid. Also, it is more suitable to the cooperation control with the other vehicle components. The EPS is now expanding to the heavier vehicle with the advance in the power electronics. In order to meet customer's needs, such as down-sizing, lower failure rate and lower price, we have developed the new motor control unit (MCU) for the EPS. The motor and the electric control unit (ECU) were integrated for the better installation. We adopted new technologies of redundant 2-drive design for more safe EPS. “2-drive Motor Control technology” which consists of dual winding, two torque sensors and two inverter drive units. In our developed MCU, even if there is a failure in one of the drive unit, the assistance of the EPS can be maintained with the other drive unit.

The objective of this study was to investigate if 3D auditory displays could be used to enhance parking assistance systems (PAS). Objective measurements and estimations of workload were used to assess the benefits of different 3D auditory displays. In today’s cars, PAS normally use a visual display together with simple sound signals to inform drivers of obstacles in close proximity. These systems rely heavily on the visual display, as the sound does not provide information about obstacles' location. This may cause the driver to lose focus on the surroundings and reduce situational awareness. Two user studies (during summer and winter) were conducted to compare three different systems. The baseline system corresponded to a system normally found in today’s cars. The other systems were designed with a 3D auditory display, conveying information of where obstacles were located through sound. A visual display was also available. Both normal parking and parallel parking was conducted.

The radar-based advanced driver assistance systems (ADAS) like autonomous emergency braking (AEB) and forward collision warning (FCW) can reduce accidents, so as to make vehicles, drivers and pedestrians safer. For active safety, automotive millimeter-wave radar is an indispensable role in the automotive environmental sensing system since it can work effectively regardless of the bad weather while the camera fails. One crucial task of the automotive radar is to detect and distinguish some objects close to each other precisely with the increasingly complex of the road condition. Nowadays almost all the automotive radar products work in bidimensional area where just the range and azimuth can be measured. However, sometimes in their field of view it is not easy for them to differentiate some objects, like the car, the manhole covers and the guide board, when they align with each other in vertical direction.

Multi-Target tracking is a central aspect of modeling the surrounding environment of autonomous vehicles. Automotive millimeter-wave radar is a necessary component in the autonomous driving system. One of the biggest advantages of radar is it measures the velocity directly. Another big advantage is that the radar is less influenced by environmental conditions. It can work day and night, in rainy or snowy conditions. In the expressway scenario, the forward-looking radar can generate multiple objects, to properly track the leading vehicle or neighbor-lane vehicle, a multi-target tracking algorithm is required. How to associate the track and the measurement or data association is an important question in a multi-target tracking system. This paper applies the nearest-neighbor method to solve the data association problem and uses an extended Kalman filter to update the state of the track.

Car Periphery Supervision (CPS) systems comprise a family of automotive systems that are based on sensors installed around the vehicle to monitor its environment. The measurement and evaluation of sensor data enables the realization of several kinds of higher level applications such as parking assistance or blind spot detection. Although a lot of similarity can be identified among CPS applications, these systems are traditionally built separately. Usually, each single system is built with its own electronic control unit, and it is likely that the application software is bound to the controller's hardware. Current systems engineering therefore often leads to a large number of inflexible, dedicated systems in the automobile that together consume a large amount of power, weight, and installation space and produce high manufacturing and maintenance costs.

Ford Motor Company has recently implemented a Hardware-In-the-Loop (HIL) testing system for a new, highly complex, hybrid electric vehicle (HEV) Electronic Control Unit (ECU). The implementation of this HIL system has been quick and effective, since it is based on proven Commercial-Off-The-Shelf (COTS) automation tools for real-time that allow for a very flexible and intuitive design process. An overview of the HIL system implementation process and the derived development benefits will be shown in this paper. The initial concept for the use of this HIL system was a complete closed-loop vehicle simulation environment for Vehicle System Controller testing, but the paper will show that this concept has evolved to allow for the use of the HIL system for many facets of the design process.

Instrument Panel Cluster (IPC), is a key ECU in vehicles. As IPC is a visual product, testing the software features of IPC is highly manual effort. Software Testing constitutes for approx. 35% of the total Software Development Life Cycle (SDLC). High focus on quick to market, shorter SDLC coupled with manual validation environment poses a challenge of increasing testing efficiency and improving software quality. This challenge drove the need to investigate a solution to automate the testing process and cut down the huge manual effort that goes into validating an Instrument Panel Cluster (IPC) software. The proposed intrusive and non-intrusive approaches to automate the testing process of IPC software employs a Frame Grabbing technique for the former approach and a Camera based technique for the latter. Both the approaches are robust, reliable, and scalable and covers the major portion of Vehicle Instrument cluster test scenarios.

This paper proposed a collision avoidance strategy that take over the control of ego vehicle when faced with urgent collision risk. To improve the applicability of collision avoidance strategy in complex scenarios, the theory of ICS (Inevitable Collision State) is introduced to evaluate the collision risk and compute the trigger flag of the system, and vehicle dynamic is taken into account when modeling ego vehicle to predict ego vehicle’s following moving. Vehicle specific characteristics including reaction time of the braking system and the braking force increasing process are taken into account. In order to reduce injury caused by collision accidents and minimize disruption to drivers, slight steering is added on top of emergency braking. The direction of the steering angle is determined according to IM (Imitating Maneuvers) The flow chart of the strategy is presented in the paper.

Reliable estimation of pumping and friction losses in modern combustion engines allows better control strategies aiming at optimal fuel consumption and emissions. Sophisticated simulation tools enable detailed simulation of losses based as well on physical and thermodynamic laws as well as on design data. Models embedded in these tools however are not real-time capable and cannot be implemented into the programs of the electronic control units (ECU's). In this paper an approach is presented that estimates the pumping and friction losses of a combustion engine with variable valve train (VVT). Particularly the pumping losses strongly depend on the control of variable valve train by ECU. The model is based on a combination of a globally physical structure embedding data driven sub models based on test bed measurements. Losses are separated concerning different component groups (bearings, pistons, etc.).

Today's vehicles use electronic control units to control engine/transmission, body and other amenities. These electronic control units are integrated into a computer network generally known as multiplexing system. There are a number of protocols, recommended practices available from SAE, ISO and other organizations for implementing the multiplexing system. This paper will provide an overview of various protocols, recommended practices available for in-vehicle networks. It provides a comparison of the physical layer of various protocols.

An autonomous vehicle is able to perceive and interpret exactly its surroundings and its interior (“Sensing”). then, it processes the information received and plan its driving strategy (“processing”). And finally, it uses its powertrain, steering and braking power to move its wheels in such a way that the planned driving strategy is put into practice (“Acting”). Testing an autonomous vehicle’s reaction to the erratic traffic scenarios using prototypes would be impractical. Physically testing these scenarios can also be risky to human life and equipment. Additionally, the repetition involved in the comprehensive testing of all these scenarios could lead to human errors. Various Self Driving car manufacturers have reported injuries and causalities while doing Functional testing [1].

Autonomous vehicle is a vehicle capable of sensing its environment and taking decisions automatically with no human interventions. To achieve this goal, ADAS (Advance Driving Assistance System) technologies play an important role and the technologies are improving and emerging. The sensing of environment can be achieved with the help of sensors like Radar and Camera. Radar sensors are used in detecting the range, speed and directions of multiple targets using complex signal processing algorithms. Radar with long range and short range are widely used in the autonomous vehicles. Radar sensors with long range can be used to realize features like Adaptive Cruise Control, Advance Emergency Brake Assist. The short-range radar sensors are used for Blind Spot Monitoring, Lane Change Assist, Rear/Front Cross Traffic Alert and Occupant Safe Exit. To realize the Autonomous vehicle functionalities four short range radar sensors are required, two on front and two on rear (left and right).

Multi-sensor fusion strategies have gradually become a consensus in autonomous driving research. Among them, radar-camera fusion has attracted wide attention for its improvement on the dimension and accuracy of perception at a lower cost, however, the processing and association of radar and camera data has become an obstacle to related research. Our approach is to build a concise framework for camera and radar detection and data association: for visual object detection, the state-of-the-art YOLOv5 algorithm is further improved and works as the image detector, and before the fusion process, the raw radar reflection data is projected onto image plane and hierarchically clustered, then the projected radar echoes and image detection results are matched based on the Hungarian algorithm. Thus, the category of objects and their corresponding distance and speed information can be obtained, providing reliable input for subsequent object tracking task.

Advanced Driver Assistance Systems (ADAS) have become an inevitable part of most of the modern cars. Their use is mandated by regulations in some cases; and in other cases where vehicle owners have become more safety conscious. Vision / camera based ADAS systems are widely in use today. However, it is to be noted that the performance of these systems is depends on the quality of the image/video captured by the camera. Low illumination is one of the most important factors which degrades image quality. In order to improve the system performance under low illumination, it is required to first enhance the input images/frames. In this paper, we propose an image enhancement algorithm that would automatically enhance images to a near ideal condition. This is accomplished by mapping features taken from images acquired under ideal illumination conditions on to the target low illumination images/frames.

In recent years the fuel injection technology has taken a great leap in two-wheeler industries both in terms of having lean emissions and in terms of improving efficiency. Especially in a market like India where fuel efficiency is given, as prime importance and fuel contamination duly exists, it becomes all the more a bigger challenge to address to these kinds of problems by some means. The main function of the fuel system is to make sure right amount of fuel is injected into the intake manifold at right time with the help of Electronic Control Unit. Variation from the right quantity and quality of fuel leads to partial burning and finally to more emissions and poor performance. So monitoring fuel pressure, there by the quantity and fuel quality is very much important to reduce emissions and to achieve desired performance.

With the development of vehicle intelligence and the Internet of Vehicles, how to protect the safety of the vehicle network system has become a focus issue that needs to be solved urgently. The Controller Area Network (CAN) bus is currently a very widely used vehicle-mounted bus, and its security largely determines the degree of vehicle-mounted information security. The CAN bus lacks adequate protection mechanisms and is vulnerable to external attacks such as replay attacks, modifying attacks, and so on. On the basis of the existing work, this paper proposes an authentication method that combines Hash-based Message Authentication Code (HMAC)-SHA256 and Tiny Encryption Algorithm (TEA) algorithms. This method is based on dynamic identity authentication in challenge/response made and combined with the characteristics of the CAN bus itself as it achieves the identity authentication between the gateway and multiple electronic control units (ECUs).

As part of the continuous development of the car, the implementation of “by-wire” solutions provides vehicle manufacturers with advantages such as weight savings, simplified modular assembly and test, and increased flexibility of physical placement. In Electro-Mechanical Braking, a type of “Brake-by-Wire”, the hydraulic control structure is replaced by electronic control units, linked to electromechanical actuators, to provide reliable and fault tolerant foundation braking of the vehicle In an effort to provide timely and effective semiconductor system solutions to our customers, Motorola Semiconductor Product Sector's Advanced Vehicle Systems Architecture group has carried out a study into the specialized requirements of EMB over the past two years. Analysis of these requirements led to the development of an architecture which used new cost-effective approaches to the problem of implementing fault tolerance.

This article describes how future Single-Chip ECU's can be debugged and calibrated using the new emerging debug standard known as GEPDIS informally named NEXUS. The paper explains why conventional debug and calibration methods no longer work with future ECU architectures. Infineon Technologies and some other leading semiconductor vendors and tool companies have worked on the development of a ‘global embedded processor debug interface standard’ called GEPDIS (1). Throughout this paper will use the GEPDIS short cut. The GEPDIS standard defines and implements a global, open, micro-controller development interface standard for embedded controller applications. The GEPDIS interface can be used for run-time Control, Code Execution and Data Trace, Calibration and Co-processor communication (for use in Rapid Prototyping).

There are a number of software-controlled features in today's automotive vehicles to meet the increasing number of requirements for comfort, safety, infotainment and reduced emissions. To meet the growing demands from such features, the software content is not only increasing rapidly, but also becoming increasingly distributed within the Electronic Control Units (ECUs), leading to the possibility of unwanted interactions and consequent loss of reliability. Therefore, the automotive software-based features have to be designed and verified using sophisticated tools and techniques. Formal methods-based techniques and tools have been used on various industrial designs over the past 6 years by one of the authors in development and applied research projects, collaborating with a number of automotive companies. The challenges faced in large projects are discussed in this context.