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Abstract A number of studies have shown that driving an unfamiliar vehicle has the potential to introduce additional risk, especially for novice drivers. However, such studies have generally used statistical methods based on analyzing crash and near-crash data from a range of driver groups, and therefore the evaluation has the potential to be subjective and limited. For a more objective perspective, this study suggests that it would be worthwhile to consider vehicle dynamic signals obtained from the Controller Area Network (CAN-Bus) and smartphones. This study, therefore, is focused on the effect of driver experience and vehicle familiarity for issues in driver modeling and distraction. Here, a group of 20 drivers participated in our experiment, with 13 of them having participated again after a one-year time lapse in order for analysis of their change in driving performance.

Abstract Recently, electric heavy-duty tractor-trailers (EHDTTs) have assumed significance as they present an immediate solution to decarbonize the transportation sector. Hence, to illustrate the economic viability of electrifying the freight industry, a detailed numerical model to estimate the battery capacity for an EHDTT is proposed for a route between Washington, DC, to Knoxville, TN. This model incorporates the effects of the terrain, climate, vehicular forces, auxiliary loads, and payload in order to select the appropriate motor and optimize the battery capacity. Additionally, current and near-future battery chemistries are simulated in the model. Along with equations describing vehicular forces based on Newton’s second law of motion, the model utilizes the Hausmann and Depcik correlation to estimate the losses caused by the capacity offset of the batteries. Here, a Newton-Raphson iterative scheme determines the minimum battery capacity for the required state of charge.

Abstract Future SAE Level 4 and Level 5 autonomous vehicles (AV) will require novel applications of localization, perception, control, and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. This article concentrates on low-speed autonomous shuttles that are transitioning from being tested in limited traffic, dedicated routes to being deployed as SAE Level 4 automated driving vehicles in urban environments like college campuses and outdoor shopping centers within smart cities. The Ohio State University has designated a small segment in an underserved area of the campus as an initial AV pilot test route for the deployment of low-speed autonomous shuttles. This article presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment.

Abstract Knowledge of intelligent vehicle absolute position is a vital premise for the implementation of decision programming, kinematic and dynamics control. In order to achieve high accuracy positioning and reduce running cost as much as possible under all operating conditions, this paper proposed an integrated positioning method based on GPS and Ultra Wide Band(UWB) for intelligent vehicle’s navigation and position system. In this method, GPS and UWB are alternately active according to the confidence level of GPS signal. When the vehicle is traveling in a wide-open area and GPS signal is well received, the positioning results of Dead Reckoning system are corrected by the low frequency positioning output from GPS. During the correcting process, in order to realize the better fusion of measurement data, a simplified federal Kalman filter was designed by using indirect method.

Abstract A detailed model for pneumatic S-cam drum brake systems is developed and integrated into a multibody dynamic model for a 33-ft A-double long combination vehicle (LCV). The model, developed in TruckSim®, is used to study the dynamics of LCVs during straight-line braking at various speeds. It includes the response delay in braking that occurs from the time of application to when the brakes are applied at the drum for all axles. Additionally, the model incorporates an accurate characterization of brake torque versus chamber pressure at different speeds, along with the anti-lock brake system (ABS) dynamics, to yield an accurate prediction of the vehicle’s deceleration during braking. The modeling results are compared with test results at speeds ranging from 20 mph to 65 mph on dry pavement. A close match between the model’s prediction and test results is observed.

Abstract Autonomous vehicles require precise knowledge of their position and orientation in all weather and traffic conditions for path planning, perception, control, and general safe operation. Here we derive these requirements for autonomous vehicles based on first principles. We begin with the safety integrity level, defining the allowable probability of failure per hour of operation based on desired improvements on road safety today. This draws comparisons with the localization integrity levels required in aviation and rail where similar numbers are derived at 10−8 probability of failure per hour of operation. We then define the geometry of the problem where the aim is to maintain knowledge that the vehicle is within its lane and to determine what road level it is on.

Abstract In order to further improve the active safety protection of the vehicle’s active collision avoidance system for vulnerable road users, consider the limitations of on-board sensors, a pedestrian active collision avoidance control strategy based on vehicle-to-vehicle (V2V) communication technology is proposed for the blind-spot dangerous scenario where pedestrians pass through the front of a stationary obstacle vehicle and collide with the host vehicle. Firstly, the relative position relationship model between the host vehicle and the pedestrian is established according to the pedestrian information detected by the obstacle vehicle sensor and the global positioning system (GPS) position information of the obstacle vehicle and the host vehicle so that the host vehicle can obtain the state information of the pedestrian in front of the obstacle vehicle through V2V communication.

Abstract Autonomous Racing is gaining increasing publicity as an attractive showcase of state-of-the-art technologies and the enhanced algorithms used for autonomous driving. The Indy Autonomous Challenge (IAC) tackled autonomous high-speed wheel-to-wheel racing at the famous Indianapolis Motor Speedway (IMS) in October 2021. To solve this problem, advanced autonomous driving algorithms were developed by each team. In this article, we display a multi-sensor localization approach developed for usage in the IAC. To decouple the lateral and longitudinal position of the ego vehicle, a trackbound coordinate system is used that can be transformed to conventional Cartesian coordinates. The longitudinal motion of the car is tracked via a modified version of the OpenVSLAM that outputs the progress of the already driven distance.

Abstract In this research, an integrated driving and braking control unit was developed for electric bikes. The unit integrates the driving and braking circuits in a module. Alternate commutation was used to design the driving and braking unit of a customized brushless direct-current hub motor (BLDCHM). The braking torque for the braking section is generated through alternating the duty cycle of the pulse-width-modulated (PWM) commands of the switching elements and phase sequence arrangement of the current conduction loops. The current conduction loops in the motor and switching elements is arranged to adjust the braking torque in a sophisticated way. The integrated design has been successfully tested in a commercialized electric bike with a BLDCHM.

Abstract It has been discussed in numerous prior studies that in-neutral coasting, or sailing, can accomplish considerable amount of fuel saving when properly used. The driving maneuver basically makes the vehicle sail in neutral gear when propulsion is unnecessary. By disengaging a clutch or shifting the gear to neutral, the vehicle may better utilize its kinetic energy by avoiding dragging from the engine side. This strategy has been carried over to series production recently in some of the vehicles on the market and has become one of the eco-mode features available in current vehicles. However, the duration of coasting must be long enough to attain more fuel economy benefit than deceleration fuel cutoff (DFCO)-which exists in all current vehicle powertrain controllers-can bring. Also, the transients during shifting back to drive gear can result in a drivability concern.

Abstract Unstable articulated vehicles pose a serious threat to the occupants driving them as well as the occupants of the vehicles around them. Articulated vehicles typically experience three types of instability: snaking, jack-knifing, and rollover. An articulated vehicle subjected to any of these instabilities can result in major accidents. In this study a Nonlinear Model Predictive Control (NMPC) that applies brake-based torque vectoring on the trailer is developed to improve the articulated vehicle stability. The NMPC formulation includes tire saturation and applies constraints to prevent rollover. The controller output is a left and right brake force allowing the longitudinal velocity change to be incorporated into the model. Simulations were conducted to instigate snaking and jack-knifing and show the NMPC controller result compared to a simple proportional controller.

Abstract In order to improve the safety of vehicles driving in mountainous areas and other curve roads, a DCT shift strategy for different drivers is designed in this article. First, the test road is digitized and an electronic map model is built based on particle filtering (PF) to achieve the optimal prediction of road conditions. Then, a driver’s intention recognition model is constructed based on principle component analysis (PCA) and Kohonen neural network (KohonenNN) to accommodate different driving intentions, and the appropriate downshift schedules are designed for different driver’s intentions. In addition, we propose the concept of the curve safety factor, and the current safety level is determined by vehicle speed and distance. By combining the earlier discussed concepts, the predictive curve shift strategy is presented.

Abstract The purpose of this article was to determine the failure safety margins of the front braking system of a Honda CTX700 motorcycle and to perform a substantive stress analysis on the system, as well as to verify the stresses using FEMAP. It should be noted that in this finite element analysis (FEA), the connections between components are modeled using linear-contact connections that exert forces on adjacent surfaces and are not trivially meshed as one solid with coincident grids with two different section material properties. The first part of the work involved accurately measuring the geometry of each part and three-dimensional (3D) modeling of all components. Measurements were taken via the trivial methods of using a ruler and caliper, and then the 3D model was generated in Solidworks by digitizing the geometric parameters. Some parts of the system were simplified in the 3D model to ensure proper meshing of the model.

Abstract The tailpipe zero-emission legislation has pushed the automotive industry toward more electrification. Regenerative braking is the capability of electric machines to provide brake torque. So far, the regenerative braking feature is primarily considered due to its effect on energy efficiency. However, using individual e-machines for each wheel makes it possible to apply the antilock braking function due to the fast torque-tracking characteristics of permanent magnet synchronous motors (PMSM). Due to its considerable cost reduction, in this article, a feasibility study is carried out to investigate if the ABS function can be done purely through regenerative braking using a mid-fidelity model-based approach. An uni-tire model of the vehicle with a surface-mount PMSM (SPMSM) model is used to verify the idea. The proposed ABS control system has a hierarchical structure containing a high-level longitudinal slip controller and a low-level SPMSM torque controller.

Abstract Many Connected and Automated Vehicle (CAV) applications assume that highly accurate positioning is always available. However, this is not the case in many real-life situations (e.g., when a satellite-based navigation system is used for positioning in urban canyons). Furthermore, very little research has been conducted to evaluate the impacts of position accuracy on CAV applications at the traffic level. The objective of this article is to investigate the positioning errors that could be tolerated by a sample of CAV applications. Toward this end, we (1) perform a general analysis of the positioning requirements of selected safety-, mobility- and environmental-focused applications and (2) examine in greater detail the effect of positioning errors on two representative CAV applications, Eco-Approach and Departure at Signalized Intersections (EAD) and High-Speed Differential Warning (HSDW).

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Abstract In view of the traditional constant spacing policy (CSP) can’t maximize the fuel saving rate of the truck platoon when choosing the smaller desired vehicle spacing as the control target, a new control strategy is proposed in this article. This strategy dramatically reduces the fuel consumption of the truck platoon from the start to the formation of a stable platoon, thus greatly increasing the fuel saving rate of the platoon. To prove the effectiveness of the strategy, this article carried out the longitudinal dynamics modeling of the truck and the modeling of the fuel consumption model of engine first. Longitudinal dynamics modeling establishes the dynamic equations for truck braking and nonbraking. The fuel consumption model of engine is built using a three-dimensional map. Second, the design of the controller is described. The controller calculates the desired acceleration of the following vehicle based on the speed error and the following distance error.