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When vehicles share certain information wirelessly via Dedicated Short Range Communications (DSRC), they enable a new layer of electronic vehicle safety that, when needed, can generate warnings to drivers and even initiate automatic preventive actions. Vehicle location and velocity provided by Global Navigation Systems (GNSS), including GPS, are key in allowing vehicle path estimation. GNSS is effective in accurately determining a vehicle's location coordinates in most driving environments, but its performance suffers from obstructions in dense urban environments. To combat this, augmentations to GNSS are being contemplated and tested. This testing has been typically done using a reference GNSS system complimented by expensive military-grade inertial sensors, which can still fail to provide adequate reference performance in certain environments.

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 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.

This document establishes techniques for validating that a mission store complies with the interface requirements contained in MIL-STD-1760 Revision D.

This SAE Information Report describes results of testing of the SAE J1746 ISP-Vehicle Standard for the communication of spatial data references between central sites and mobile vehicles on roads. Testing was performed by the Oak Ridge National Laboratory and its contractors, resulting in a document from which this Information Report has been extracted. Tests were performed by computer analysis and corroborated by field tests with a mobile vehicle.

A key aim of research into cell phone tasks is to obtain an unbiased estimate of their relative risk (RR) for crashes. This paper re-examines five RR estimates of cell phone conversation in automobiles. The Toronto and Australian studies estimated an RR near 4, but used subjective estimates of driving and crash times. The OnStar, 100-Car, and a recent naturalistic study used objective measures of driving and crash times and estimated an RR near 1, not 4 - a major discrepancy. Analysis of data from GPS trip studies shows that people were in the car only 20% of the time on any given prior day at the same clock time they were in the car on a later day. Hence, the Toronto estimate of driving time during control windows must be reduced from 10 to 2 min.

This paper presents a novel methodology to develop and validate fuel consumption models of Refuse Collecting Vehicles (RCVs). The model development is based on the improvement of the classic approach. The validation methodology is based on recording vehicle drive cycles by the use of a low cost data acquisition system and post processing them by the use of GPS and map data. The corrected data are used to feed the mathematical energy models and the fuel consumption is estimated. In order to validate the proposed system, the fuel consumption estimated from these models is compared with real filling station refueling records. This comparison shows that these models are accurate to within 5%.

We present a method for the estimation of vehicle mass and road gradient for a light passenger vehicle. The estimation method uses information normally available on the vehicle CAN bus without the addition of extra sensors. A composite parameter estimation algorithm incorporating a nonlinear adaptive observer structure uses vehicle speed over ground and driving torque to estimate mass and road gradient. A system of filters is used to avoid deriving acceleration directly from wheel speed. In addition, a novel data fusion method makes use of the regressor structure to introduce information from other sensors in the vehicle. The dynamics of the additional sensors must be able to be parameterised using the same parameterisation as the complete vehicle system dynamics. In this case we make use of an Inertial Measurement Unit (IMU) which is part of the vehicle safety and Advanced Driver Assist Systems (ADAS).

This paper presents a new way to evaluate vehicle speed profile aggressiveness, quantify it from the perspective of the rapid speed fluctuations, and assess its impact on vehicle fuel economy. The speed fluctuation can be divided into two portions: the large-scale low frequency speed trace which follows the ongoing traffic and road characteristics, and the small-scale rapid speed fluctuations normally related to the driver's experience, style and ability to anticipate future events. The latter represent to some extent the driver aggressiveness and it is well known to affect the vehicle energy consumption and component duty cycles. Therefore, the rapid speed fluctuations are the focus of this paper. Driving data collected with the GPS devices are widely adopted for study of real-world fuel economy, or the impact on electrified vehicle range and component duty cycles.

This paper conducts an investigation on the operating cycle of Bus No. 306, which is equipped with wireless charging system, in Changsha, Hunan Province, China. The wireless charging system and electric buses are manufactured by ZTE Corporation (Zhongxing Telecommunication Equipment Corporation) and BYD Company Limited, respectively. In this paper, the operating cycle is quantified and modeled based on experimental data. The real-time bus route and SOC (state of charge) during daytime operation are recorded with the help of GPS (global position system) and BMS (battery management system). The wireless charging process is tested with a power analyzer and its charging efficiency is compared with a plug-in system. Besides, the radiation level while charging is also taken into consideration. Currently, the buses are designed to operate in daytime and get charged at night.

System identification is an important aspect in model-based control design which is proven to be a cost-effective and time saving approach to improve the performance of hybrid electric vehicles (HEVs). This study focuses on modeling and parameter estimation of the longitudinal vehicle dynamics for Toyota Prius Plug-in Hybrid (PHEV) with power-split architecture. This model is needed to develop and evaluate various controllers, such as energy management system, adaptive cruise control, traction and driveline oscillation control. Particular emphasis is given to the driveline oscillations caused due to low damping present in PHEVs by incorporating flexibility in the half shaft and time lag in the tire model.

To avoid a trial and error adjustment for designing EMI filters, clarifying load impedance of operating condition, i.e., dynamic impedance of equipment is very useful. Therefore the need to a non-contact measurement method of the impedance connected to a wire harness is increasing rapidly. A measurement method using a network analyzer with two current probes was previously proposed. However, it was confirmed only up to 30 MHz. Many radio equipment operate above 30 MHz such as FM receivers and GPS receivers installed in vehicles. So increasing the measurement frequency is necessary in the auto industry. At first, we tried to expand the applicable frequency to 100 MHz, i.e., FM band. In this study, we applied the transmission line theory using the non-contact measurement method. Furthermore, in order to use the theory, the characteristic impedance and phase constant of the wire harness are required. So we made an additional measurement to estimate them.

The unmanned roller is one of the most popular construction vehicles, for which the accurate path-following is one of the most important control task. The dual-antenna Global Positioning System (GPS), usually mounted on the top of the cabin and the front drum separately, is used to approximately measure the position and heading direction at the contact patch between the wheel and the road. However, in the presence of large variation in the attitude of the roller, caused by the uneven construction site, there is bias in position and heading measurement due to the wobble of the roller. Obviously, this introduces several disturbances to the path-following control. In this paper, the Attitude Heading Reference System (AHRS) is used to measure the attitude information thereby corrects the position and heading of the roller measured by GPS only.

The aim of this paper is to present a novel integrated navigation system, based on the fusion between the Global Positioning System (GPS) and low-cost vehicle onboard dynamic sensors for autonomous vehicle positioning problems. In this system, the information of vehicle’s angular rotation is applied to dead reckoning (DR) module based on the Unscented Kalman Filter (UKF) to provide the vehicle position information during GPS outage. In the DR module based on UKF, vehicle onboard dynamic sensors, include wheels speed sensors, accelerometer, and steering angle sensor, are utilized to estimate the vehicle yaw rate, while the traditional method using IMU sensor is relatively expensive. Also, the vehicle dynamic model is employed in the estimation of yaw rate, which can provide better accuracy than the traditional kinematic model. To validate the effectiveness of the integrated navigation system, tests are carried out on a small-scale vehicle platform.

The platooning of connected automated vehicles (CAV) possesses the significant potential of reducing energy consumption in the Intelligent Transportation System (ITS). Moreover, with the rapid development of eco-driving technology, vehicle platooning can further enhance the fuel efficiency by optimizing the efficiency of the powertrain. Since road grade is a main factor that affects the energy consumption of a vehicle, the estimation of the road grade with high accuracy is the key factor for a connected vehicle platoon to optimize energy consumption using vehicle-to-vehicle (V2V) communication. Commonly, the road grade is quantified by single consumer grade global positioning system (GPS) with the geodetic height data which is rough and in the meter-level, increasing the difficulty of precisely estimating the road grade.

High-precision and real-time ego-motion estimation is vital for autonomous vehicle. There is a lot GPS-denied maneuver such as underground parking lot in urban areas. Therefore, the localization system relying solely on GPS cannot meets the requirements. Recently, lidar odometry and visual odometry have been introduced into localization systems to overcome the problem of missing GPS signals. Compared with visual odometry, lidar odometry is not susceptible to light, which is widely applied in weak-light environments. Besides, the autonomous parking is highly dependent on the geometric information around the vehicle, which makes building map of surroundings essential for autonomous vehicle. We propose a lidar inertial odometry and mapping. By sensor fusion, we compensate for the drawback of applying a single sensor, allowing the system to provide a more accurate estimate.

People through many years of experience, have developed a great intuitive sense for navigation and spatial awareness. With this intuition people are able to apply a near rules based approach to their driving. With a transition to autonomous driving, these intuitive skills need to be taught to the system which makes perception is the most fundamental and critical task. One of the major challenges for autonomous vehicles is accurately knowing the position of the vehicle relative to the world frame. Currently, this is achieved by utilizing expensive sensors such as a differential GPS which provides centimeter accuracy, or by using computationally taxing algorithms to attempt to match live input data from LiDARs or cameras to previously recorded data or maps. Within this paper an algorithm and accompanying hardware stack is proposed to reduce the computational load on the localization of the robot relative to a prior map.

The emphasis in this standard is the development of data word and message formats for AS15531 or MIL-STD-1553 data bus applications. This standard is intended as a guide for the designer to identify standard data words and messages for use in avionics systems and subsystems. These standard words and messages, as well as the documentation format for interface control document (ICD) sheets, provide the basis for defining 15531/1553 systems. Also provided in this standard is the method for developing additional data word formats and messages that may be required by a particular system but are not covered by the formats provided herein. It is essential that any new word formats or message formats that are developed for a 15531/1553 application follow the fundamental guidelines established in this standard in order to ease future standardization of these words and messages. The standard word formats presented represent a composite result of studies conducted by the U.S.

The emphasis in this standard is the development of data word and message formats for AS15531 or MIL-STD-1553 data bus applications. This standard is intended as a guide for the designer to identify standard data words and messages for use in avionics systems and subsystems. These standard words and messages, as well as the documentation format for interface control document (ICD) sheets, provide the basis for defining 15531/1553 systems. Also provided in this standard is the method for developing additional data word formats and messages that may be required by a particular system but are not covered by the formats provided herein. It is essential that any new word formats or message formats that are developed for a 15531/1553 application follow the fundamental guidelines established in this standard in order to ease future standardization of these words and messages. The standard word formats presented represent a composite result of studies conducted by the U.S.

The scope of this document is limited to Enhanced Synthetic Vision Systems ESVS human factors considerations and requirements in comprehension, interpretation and application of imagery and integrated symbology in Enhanced (sensor) and Synthetic (database) Vision Systems in aircraft. Any overlap into logic problems or hardware/software design should be considered to be incidental to the human factors issues. Where the performance characteristics of specific technologies are relevant they will be identified, and where performance criteria are relevant to specific intended functions/use they will be identified. From a regulatory view, intended function (Guidance or Information/Situation Awareness support) has a tremendous effect upon the design of an ESV System.