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Vehicle Crash Reconstruction Principles and Technology

2021-06-15
Crash reconstruction is a scientific process that utilizes principles of physics and empirical data to analyze the physical, electronic, video, audio, and testimonial evidence from a crash to determine how and why the crash occurred. This course will introduce this reconstruction process as it gets applied to various crash types - in-line and intersection collisions, pedestrian collisions, motorcycle crashes, rollover crashes, and heavy truck crashes. Methods of evidence documentation will be covered. Analysis methods will also be presented for electronic data from event data recorders and for video.
Technical Paper

Research on Vehicle Lane Change Based on Vehicle Speed Planning

2021-04-06
2021-01-0162
Lane changing manoeuvers is an essential rudiment in vehicle driving and has a significant impact on the characteristics of traffic flow. In the case of traditional cars, the driver operates the vehicle to complete the lane change whilst for autonomous vehicles, completing the lane change requires planning the lane change trajectory and controlling the vehicle speed during the lane change. Unreasonable lane change trajectory and vehicle speed may cause the vehicle to lose stability, threaten driving safety, increase energy consumption and waste energy. This paper considers the safety and economy of the lane changing process, and proposes a new lane changing method for vehicles.
Technical Paper

Assessing the Access to Jobs by Shared Autonomous Vehicles in Marysville, Ohio: Modeling, Simulating and Validating

2021-04-06
2021-01-0163
Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting.
Technical Paper

A Hybrid method for Automotive Entity Recognition

2021-04-06
2021-01-0179
Over the past decades, automotive industry has made substantial investments in automation solutions, electric and autonomous vehicles and advanced product technologies for enhancing vehicular communication and so on. The rise of industry 4.0 brings out a revolutionary transformation in the automotive industry with low-cost computing, high-speed connectivity, and machine learning that have enabled the digitization of the physical world, transforming insights into optimized actions. These technologies have an important role in the growth and future of automotive domain. Hence it is relevant and important to get insight of different OEMs (Original Equipment Manufacturer) at different geographical locations and their focusing technologies adapted currently and in the nearby future. In this paper, we are implementing a hybrid method for entity recognition, which is a combination of both rule-based and machine learning based entity recognition techniques.
Technical Paper

Predictive Gearbox Oil temperature using Machine Learning

2021-04-06
2021-01-0182
Gearbox is one of the most defining components for vehicles, turbines and other applications. A failure in the gearbox would ultimately cause the system to breakdown and thus results in operational failure. A gearbox failure can be attributed to several factors such as gearbox oil temperature, driving patterns, dependent engine components and other various gearbox performance.The focus of this paper is gearbox oil temperature sensor which is the one important factor that determine gearbox overheating and influence the system to take precautionary steps in switching from different types of oil to prevent risk of damaging their equipment and expensive repair .The goal of this paper is to predict the gearbox oil temperature sensor failure by adopting machine learning techniques.Various machine learning techniques such as Support vector machine, decision trees and random forest etc are employed in this paper to achieve the objective.
Technical Paper

A Real-time Curb Detection Method for Vehicle by Using a 3D-LIDAR Sensor

2021-04-06
2021-01-0076
Effectively detecting road boundaries in real time is critical to the applications of autonomous vehicles, such as vehicle localization, path planning and environmental understanding.To precisely extract the road boundaries from the 3D LiDAR data, a real-time curb detection algorithm consisting of four steps is proposed in this paper.
Technical Paper

Arrangement and Control Method of Cooperative Vehicle Platoons

2021-04-06
2021-01-0113
With the development of cellular communication technology and for the sake of reducing drag resistance, the multi-lane platoons technology will be more prosperous in the future. In this article, the cooperative vehicle platoons method on the public road is represented. The method’s architecture is composed of the following parts: behavior decision, path planning and vehicle dynamic control. The behavior decision uses the finite state machine to make decision and judgment on the cooperative lane change of vehicles, and starts to execute the lane change step when the lane change conditions are met. In terms of path planning, with the goal of ensuring comfort, the continuity of the vehicle state and no collision between vehicles, a fifth-order polynomial is used to fit every vehicle trajectory. In terms of vehicle dynamic control, a model predictive control algorithm is used to solve the multi-vehicle centralized optimization control problem.
Technical Paper

Decision-Making for Autonomous Mobility Using Remotely Sensed Terrain Parameters in Off-Road Environments

2021-04-06
2021-01-0233
Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps many resources are needed and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach on the data acquired from the Keweenaw Research Center terrains and present some results. DISTRIBUTION A.
Technical Paper

Supervised terrain classification with adaptive unsupervised terrain assessment

2021-04-06
2021-01-0250
Off road navigation demands ground robots to traverse complex and often changing terrain. Classification and assessment of terrain can improve path planning strategies by reducing travel time and energy. In this paper we introduce a terrain classification and assessment framework that relies on both exteroceptive and proprioceptive sensor modalities. The robot captures an image of the terrain it is about to traverse and records corresponding vibration data during traversal. These images are manually labelled and used to train a support vector machine (SVM) in an offline training phase. Images have been captured under different lighting conditions and across multiple locations to achieve diversity and robustness to the model. Acceleration data is used to calculate statistical features that capture the roughness of the terrain whereas angular velocities are used to calculate roll and pitch angles experienced by the robot.
Technical Paper

Study on Selective Electroplating for Pattern/Lighting on Plastic

2021-04-06
2021-01-0367
For making metal touch feeling and lighting simultaneously, selective electroplating is widely applied in button, panel and etc. in interior/exterior parts of automotive. In this paper, new selective electroplating with printing are suggested an alternative manufacturing process of two shot molding, PC (Polycarbonate) and ABS (Acrylonitrile-Butadiene-Styrene). Manufacturing process of selective electroplating is as follows: letter and symbol are printed with masking ink for preventing electroplating by pad printing on plastic part with PC+ABS and then pass through plating line. After electroplating process, the part has electroplated metal layer except for the printed area. As main resin of ink for preventing electroplating is transparent PVC (Polyvinyl Chloride), printed area does not react with chemicals of etching and electroless plating, so the metal layer by electroplating is not formed. And new PC+ABS is developed for reducing the yellowness and increasing light transmission.
Technical Paper

Review and Comparison of Different Multi-channel Spatial-phase Shift Algorithms of Electronic Speckle Pattern Interferometry

2021-04-06
2021-01-0304
Electronic Speckle Pattern Interferometry (ESPI) is one of most sensitive and accurate methods for 3D deformation measurement in micro and sub micro-level. ESPI measures deformation by evaluating the phase difference of two recorded speckle interferograms under different loading conditions. By a spatial phase shift technique, ESPI enables a phase measurement with a single image (multi images are usually required in the temporal phase shift technique) and, thus, it allows for the rapid, accurate and continuous 3D deformation measurement. Multi-channel and carrier frequency are the two main methods of spatial phase shift. Compared with carrier frequency method, which is subject to the problem of spectrum aliasing, multi-channel method is more flexible in use. For extracting the phase value of speckles, three-step algorithm, four-step algorithm and five-step arbitrary phase algorithm are commonly used.
Technical Paper

Developing Prediction Based Algorithms for Energy and Exergy Flow Characterizations

2021-04-06
2021-01-0258
The future battlefield will include multiple dissimilar manned and unmanned aerial, ground, sea, and space vehicles working in concert with each other to support fires, logistics, maneuvers, communication, and coordination-based missions. Mission effectiveness and efficiency are often at odds, and due to the distributed and dissimilar energy flows inherent in Multi-Domain Operations (MDO) there is a need to understand, identify, and characterize the energy flows. The ability to analyze the energy flows and effectively maintain adequate energy reserves could provide strategic capabilities to the warfighters, permitting energy informed operations to maximize mission effectiveness and efficiency, while mitigating vulnerabilities. This research focuses on developing energy and exergy characterization through development of Artificial Intelligence (AI), Machine Learning (ML), and Artificial Neural Networks (ANNs) for assessing and analyzing performance of a platform.
Technical Paper

Three Failure Models for CFRP Composites

2021-04-06
2021-01-0310
Several failure criteria and stiffness degradation laws for composite materials were summarized and compared from the points of precision and convenience of use. The 2D/3D Hashin failure criteria were matched with the Tan’s, Tserpes and 3D Zinoviev stiffness degradation rules. Three new failure models including 2D Hashin-Tan, 3D Hashin-Tser and 3D Hashin-Zin were presented for CFRP materials. The above three models were coded and incorporated into the ABAQUS software by user subroutines, among which model 2D Hashin-Tan and model 3D Hashin-Tser were programmed using the implicit algorithm VUSDFLD while model 3D Hashin-Zin was coded using the explicit algorithm VUMAT. Experiments of uniaxial tension and three-point bending were performed. A single element subjected to uniaxial tension and three-bending were simulated to check the function and precision of the new models.
Technical Paper

Research and Parameter Optimization on Ride Comfort and Road Friendliness of Interconnected Air Suspension for Commercial Vehicles

2021-04-06
2021-01-0316
In order to improve the ride comfort and road friendliness of heavy commercial vehicles, a lateral interconnected air suspension system is developed. The lateral interconnected air suspension exchanges gas between air springs to reduce body vibration and tire load on the ground. Based on the theory of thermodynamics and vehicle dynamics, a 10-DOF vehicle dynamics model with lateral interconnected air suspension is established. Interconnected pipeline parameters and excitation frequency’ influence on characteristics of air suspension system in whole vehicle are calculated and analyzed. Simulation results show that the stiffness of air suspension decreases gradually with the increase of interconnected pipeline diameter and the damping of air suspension decreases gradually with the increase of excitation frequency. The designed interconnected air spring experiments verify the simulation results.
Technical Paper

Advanced Analytical Truck Tires-Terrain Interaction Model

2021-04-06
2021-01-0329
This paper focuses on developing an advanced analytical tire-terrain interaction model for full vehicle performance prediction purposes. The truck tire size 315/80R22.5 is modeled using Finite Element Analysis (FEA) technique and validated against manufacturer experimental data in static and dynamic domains. While the terrain is modeled using Smoothed-Particle Hydrodynamics (SPH) technique and calibrated using experimental results of pressure-sinkage and direct shear tests. The contact between FEA tire model and SPH soil model is defined using the node symmetric node to segment with edge treatment algorithm. The model setup consists of four tires appended back to back over a box filled with soil particles to represent a multi-axle off-road truck. The distances between the four tires are similar to the distances between the four axles of an off-road truck.
Technical Paper

Collision Avoidance Strategy of High-speed AEB System Based on Mnimum Safety Distance

2021-04-06
2021-01-0335
AEB system is an important part of automobile active safety, which can effectively reduce rear-end collision accidents and protect drivers' safety through active braking. AEB system has been included in many countries' new car assessment programme as the test content of active safety. In view of obviously deficiencies of the existing AEB control algorithm in avoiding longitudinal collision at high speed, it is proposed to an optimized model of the minimum safe distance for rear-end collision prevention on high-speed road in order to improve the accuracy of AEB system. Considering the influence of road adhesion coefficient and human comfort on the maximum braking deceleration, it is established to a more accurate and reasonable AEB system to avoid collision for expressway. The collision avoidance strategy is verified by simulation software.
Technical Paper

Pneumatic Power Unit for a Wheeled Vehicle

2021-04-06
2021-01-0640
The article theoretically substantiates the choice of the full-load curve of a pneumatic power unit and a pneumatic power unit combined with an internal combustion engine by the example of a compact wheeled vehicle. The aim is to prove the possibility of using a pneumatic power unit for moving the compact wheeled vehicle taking into account work processes of a pneumatic power unit and an internal combustion engine. The unique feature of the considered theoretical approach in justifying the choice of the full-load curve of a pneumatic power unit and a pneumatic power unit combined with an internal combustion engine is comparison of operating modes of units being a part of a vehicle and the using the capacity of different units combination with similar burn processes at 800–1000 RPM-1.
Technical Paper

Team AVERERA’s Alterno V4.0 – A Hyper Fuel-Efficient Electric Prototype Vehicle for Shell Eco-Marathon

2021-04-06
2021-01-0792
Team Averera is a group of automobile enthusiasts students from the Indian Institute of Technology Banaras Hindu University. The team works under the Center for Energy and Resources development. The team designs and develops fuel-efficient vehicle prototypes and take part in Shell Eco-Marathon Asia each year. The competition demands student teams to design and build prototype vehicles with a focus to achieve maximum fuel efficiency. After 3 successful attempts in the competition since 2015, the team builds a completely redesigned fourth version named Alterno V4.0. The vehicle represented IIT BHU and INDIA in the 2018 and 2019 chapters of the event. The vehicle clocked the highest fuel efficiency of 465.1km/kwhr in the 2019 chapter of Shell Eco-Marathon Asia 2019 held at Sepang International F1 Circuit, Malaysia, and stood 2nd in the event.
Technical Paper

Implementation and Validation of Behavior Cloning using Scaled Vehicles

2021-04-06
2021-01-0248
Over the last decade, a tremendous amount of research and progress has been made towards developing smart technologies and strategies to implement the lane-keeping assist, lane following algorithms. One of the fundamental objectives for the development of such technologies is to enable vehicles to maneuver autonomously with the capability to avoid obstacles, keeping lanes, and thereby maintain safety. Driving is one of the riskiest activities we might choose to do during one’s lifetime. The autonomous market is currently growing at an existential rate and many driverless vehicles are expected to be on our roads this year and in large numbers. Implementing the new autonomy strategies straight away on the full-scaled vehicles increases the complexity concerning the cost incurred and safety of the environment. The alternate approaches to test the strategy include simulation which was ruled out as many real-life instances are difficult to recreate and incorporate.
Technical Paper

Road-Shoulder Scanning for Minimum Risk Maneuver In Semi-Automated Driving Using Multi-sensor Kalman Filter

2021-04-06
2021-01-0867
Based on SAE’s classification, level 3 driving automation and beyond will be capable to free human drivers while semi-automated driving features are engaged. However, semi-autonomous vehicles may encounter scenarios that under which, the semi-autonomous driving features might not be available or safe. Such scenarios include sensor/actuator malfunction, hazardous road-condition, etc. To comply with safety requirement, semi-automated vehicles should be able to perform Minimum Risk Maneuver (MRM) into road-shoulder whenever semi-autonomous mode isn’t possible. Determining availability of the road shoulder is a key to the MRM, but is also a challenging problem given state of art perception signals. None of the perception signals are robust enough to correctly detect shoulder availability for all circumstances. In this paper, a multi-sensor Kalman Filter is utilized to fuse multiple perception signals from radar, vision, and drivable corridors.
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