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This paper presents the conceptual design of a high-power, high-speed tractor-trailer for severe duty applications. Design of the tractor-trailer introduces several new concepts, including the general vehicle architecture, a new electrical transmission system and a new electric tandem axle. The vehicle architecture consists of a low drag cab concept with a fully integrated turbo-generator power source, an exhaust gas electric decontamination system and auxiliaries. The electric transmission introduces a new combination of electrical machines and power electronics designed to perform under maximum load with minimum dimension, weight and price. The electric tandem axle is a new concept of an all-wheel steering independent suspension with virtual electromagnetic differential.

3-D horn is a vehicle to vehicle communication-based technology which helps in reducing the noise pollution, which occurs, due to honking of automobile horns by letting only the drivers of the automobile to hear the horns and not the whole environment around him. To achieve this, several relatively small horn speakers are placed inside the car. These speakers are controlled by drivers of other cars. In this way honking will be heard only by the drivers. The most unique feature of this technology is the 3-D effect caused by the speakers which will let the driver know the location of the outside car which is honking. The 3-D effect is achieved by varying the intensity and proper allotment of sound to the positioned speakers in such a way that it will give the feel of the location of the outside car to the driver. Human detection is another important feature this technology provides. It will recognize whether the horn is honked for an automobile or for a human.

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.

The significance and the number of vehicle safety features enabled via connectivity continue to increase. OnStar, with its automatic airbag notification, was one of the first vehicle safety features that demonstrate the enhanced safety benefits of connectivity. Vehicle connectivity benefits have grown to include remote software updates, data analytics to aid with preventative maintenance and even to theft prevention and recovery. All of these services require available and reliable connectivity. However, except for the airbag notification, none have strict latency requirements. For example, software updates can generally be postponed till reliable connectivity is available. Data required for prognostic use cases can be stored and transmitted at a later time. A new set of use cases are emerging that do demand continuous, reliable and low latency connectivity. For example, remote control of autonomous vehicles may be required in unique situations.

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.

Indian automobile production increased at a CAGR of 12.2% over FY05-FY13, with a decline in Commercial Vehicle (CV) growth rate during FY09 and FY13. Globally, automotive industry suffered a decline in FY09 due to the global financial crisis and again on a decline in FY12 due to the European sovereign debt crisis. Apart from the global events, there are various internal risks the Indian OEMs need to consider: 1) regulatory risk due to excise duty hikes, decontrol of fuel pricing, etc., 2) market risks due to currency, inflation, interest rates, material cost, 3) industry risks due to increased competition, price war, etc. In this scenario, Indian Original Equipment Manufacturers (OEMs) need to constantly recalibrate their strategies to the changing market dynamics and associated risks. A research on megatrends affecting the Indian CV industry has identified more focus on Total Cost of Ownership (TCO) as one of the megatrend.

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.

Supported by generous federal incentives and customer acceptance, the number of plug-in electric vehicles (PEVs) is increasing worldwide. A PEV can be charged both at a residential or a commercial facility by connecting it to the power grid using a charging equipment. The power demand for a single PEV charging may exceed the peak power demand of an ordinary household. Hence, an increasing number of PEVs can potentially impact the power grid massively. This paper aims to quantify the impacts of an increasing number of PEVs on power distribution circuits. A comparative analysis of the PEV impacts on the North American and European distribution circuits are presented. The study also identifies the indicators representative for quantifying the grid impacts of PEV charging. The paper further intents to clarify the interface between OEMs and utilities in achieving the common goal of a successful PEV charging and therefore a faster customer acceptance.

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).

A comparison of various numerical tools and techniques was performed for calculating the lightning indirect effects to composite structures and internal systems. This paper is a summary of the initial comparison results. Detailed results of each technique considered are given in additional separate papers presented during this conference. The modeling considered current distributions over and within composite surfaces and the coupling of current and voltages to internal systems such as wire bundle cables and hydraulic and fuel tubes. The models were compared to each other and to measured data from low level swept continuous wave (LLCW) tests performed on two test fixtures. Other features of the codes such as run time, ease of use, computer requirements, availability of documentation and technical support, etc. are compared as well.

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.

The majority of road accidents are caused by human oversight. Advanced Driving Assistance System (ADAS) has the potential to reduce human error and improve road safety. With the rising demand for safety and comfortable driving experience, ADAS functions have become an important feature when car manufacturers developing new models. ADAS requires high accuracy and robustness in the perception system. Camera and radar are often combined to create a fusion result because the sensors have their own advantages and drawbacks. Cameras are susceptible to bad weather and poor lighting condition and radar has low resolution and can be affected by metal debris on the road. Clustering radar targets into objects and determine whether radar targets are valid objects are challenging tasks. In the literature, rule-based and thresholding methods have been proposed to filter out stationary objects and objects with low reflection power.

With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs.

The present and futuristic surround view camera systems provide the bird-eye’s view of the driving environment to the driver through a real-time video feed in the digital cockpit infotainment display, which assists the driver in maneuvering, parking, lane changing by performing object detection, object tracking, maneuver estimation, blind spot detection, lane detection, etc., The functional safety of this surround view camera system is gets compromised, if it fails to alert the driver, when truly obstacles are present in the nearby driving environment of the vehicle, or if it alerts the driver when no obstacles are present in the nearby driving environment. This malfunctioning of surround view driver assistance system is due to integrity compromisation through cyberattacks, where attackers forge the displayed video data on the infotainment system, which has external world connectivity.

Cyber-physical systems are joint instances of growing complexity and high integration of elements in the information and physical domains reaching high levels of difficulty to engineer an operate them. This happens with satellites, aircraft, automobiles, smart grids and others. Current technologies as computation, communication and control integrate those domains to communicate, synchronize and operate together. However, the integration of different domains brings new challenges and adds new issues, mainly in real time distributed control systems, beginning with time synchronization. In this paper, we present a discussion on time synchronization and their effects in distributed cyber-physical control systems. To do that, we review the literature, discuss some time synchronization techniques used in cyber-physical systems, and illustrate them via model and simulation of a system representative of the aerospace area.

Commercial vehicle operators and governments around the world are looking for ways to cut down on fuel consumption for economic and environmental reasons. Two main factors affecting the fuel consumption of a vehicle are the drive route and the driver behavior. The drive route can be specified by information such as speed limit, road grade, road curvature, traffic etc. The driver behavior, on the other hand, is difficult to classify and can be responsible for as much as 35% variation in fuel consumption. In this work, nearly 600,000 miles of drive data is utilized to identify driving behaviors that significantly affect fuel consumption. Based on this analysis, driving scenarios and related driver behaviors are identified that result in the most efficient vehicle operation. A driver assistance system is presented in this paper that assists the driver in driving more efficiently by issuing scenario specific advice.