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IoT-Enabled Vision System for Detection of Tint Level
Published in B.K. Tripathy, J. Anuradha, Internet of Things (IoT), 2017
From the ROI, we can find the ratio of height and width of the vehicle which is directly related with the width and breadth of image rectangle. From this obtained ratio, the type of vehicle is determined such as hatchback or notchback (sedan class). The windscreen/window area generally changes according to the type of vehicle. For example, in the sedan class vehicles, the windscreen location is nearly at the middle whereas in hatchback vehicles, it is slightly at the rear side of the vehicle. Thus, the vehicle tint location can be easily obtained for different class of vehicles. Once the vehicle class/type is extracted, then we can extract the windshield/window tint area of the vehicle and on this cropped area we apply our three basic techniques of tint detection, namely contour detection, histogram analysis, and color segmentation.
Introduction Automotive Product Development
Published in Vivek D. Bhise, Automotive Product Development, 2017
An automotive PD program generally extends over 12 to 48 months, depending on the scope of the program and how the beginning and end points of the program are defined. A large PD program may involve developing a totally new vehicle platform, a new powertrain, and one or more product variations, for example, similar body-style but different exterior panels and interior components for different corporate brands (e.g., Chevrolet, Buick, and Cadillac; Toyota and Lexus; Ford and Lincoln), or adding more body-styles or variants (e.g., sedan, coupe, hatchback, station wagon, and SUV). A large vehicle program may thus extend over several years. A small program may involve merely refreshing an existing vehicle with minor changes to vehicle exterior, such as changes in front fascia, grill, wheel covers, exterior colors, headlamps and tail lamps, and other minor changes to the interior, such as changes in audio components, graphics, and interior materials and colors. A small vehicle program may take from a few months to about 18 months to complete its vehicle development activities.
Real-time monitoring of the extended road network by utilising telematics technology
Published in Sandra Erkens, Xueyan Liu, Kumar Anupam, Yiqiu Tan, Functional Pavement Design, 2016
A road section (Figure 2) which has the presence of various different defects and roughness was identified to develop a model which could be used to estimate the condition of the road by utilising the data harvested from telematics devices. To generate a preliminary model, three passenger vehicles (including a small hatchback, large hatchback, and a large Sports Utility Vehicle (SUV)) were driven at four different speeds (i.e. 40 km/h, 60 km/h, 80 km/h and varying speed) over a continuous 2 km road section. The constant speeds were achieved by using the vehicles cruise control systems. Varying speed was achieved when the driver randomly varied the speed over the length of road. To ensure the data are calibrated for all the different variabilities, the telematics devices were also installed in different locations in each vehicle type. Calibration by correlation involved calibrating the telematics device profile data with the actual road profile as measured with a Class 1 profilometer.
Minimising drag coefficient of a hatchback car utilising fractional factorial design algorithm
Published in European Journal of Computational Mechanics, 2018
Mehrdad Vahdati, Sajjad Beigmoradi, Alireza Batooei
In this research, enhancement of aerodynamic performance for a facelifted C-segment hatchback car is surveyed. Face lift is performed on rear end of the car to convert sedan to hatchback. Freezing other panels and parts except rear end is determined as one of the study’s hard point. To this end, five design variables of rear end are chosen to be optimised. These parameters are (1) rear spoiler length (L), (2) rear spoiler angle (), (3) fifth door height (H), (4) rear lamp boat tail angle () and (5) rear diffuser angle (). The range for variations of these parameters is determined by industrial design department. Figure 1 and Table 2 depict design variables and their variation’s range, respectively.
Optimal platform design using non-dominated sorting genetic algorithm II and technique for order of preference by similarity to ideal solution; application to automotive suspension system
Published in Engineering Optimization, 2018
Mohammad Hassan Shojaeefard, Abolfazl Khalkhali, Hamed Faghihian, Masoud Dahmardeh
This article employs a novel approach in designing an optimal common suspension system for an automotive platform. Five different types of automotive—sedan, hatchback, sport utility vehicle (SUV), minivan car and a pickup—are considered as the platform variants. First, a five-degrees-of-freedom car ride model is presented. including broadband stochastic roadway inputs which are described by the displacement power spectral density. Optimal design of the suspension system for the product family is carried out using the popular multi-objective GA method (NSGA-II) to find the non-dominated optimal points, while considering not only the performance of the suspension system, but also the level of commonality as the objective functions. The design constraints that are taken into account are road holding, maximum wheel travel, tyre deflection, and damping ratio for the vehicle shock absorbers. In the second step, the TOPSIS method is employed to find the trade-off design point out of all non-dominated points obtained for the suspension system of the automotive product family. The obtained results indicate the effectiveness of the proposed method.
Analysis of rear-end crash potential and driver contributing factors based on car-following driving simulation
Published in Traffic Injury Prevention, 2022
Lerdmanus Bumrungsup, Kunnawee Kanitpong
The calculated distance headway is subtracted by the length of the vehicle ahead to obtain the clearance between vehicles. The clearance is used to determine the rear-end crash potential as shown in Equation 1 and 2. Five meters were assumed as the average car length for different types of sedan/hatchback, pickup truck, and van. The maximum length of large vehicle based on Thailand Land Transport Act (1979) were used to define the length of large vehicle in this study. The length of 15 meters was used for buses and semi-trailer trucks, while 25 meters were applied to double-trailer trucks.