Search Results

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 Management of expressway networks has been mainly focused on defect management without looking at the correlations with accidental risks. This causes unsustainability in expressway infrastructure maintenance since such defects may not be a contributing factor toward public safety. Thus it is necessary to incorporate accidental events for decision-making in infrastructure management. This study has developed a novel approach to machine learning (ML) that incorporates actual primary data from the last 10 years of single-vehicle accidents (SVA) by collisions with motorway facilities, or so-called single-vehicle collisions with fixed objects. The ML is firstly aimed at identifying the influential factors of SVA in relation to finding effective countermeasures for accidents by integrating the correlation analysis, multiple regression analysis, and ML techniques. The study reveals that wet pavement conditions have a significant effect on SVA.

Abstract A wind-tunnel study was undertaken to investigate the drag reduction potential of two-truck platooning, in the context of understanding some of the factors that may influence the potential fuel savings and greenhouse-gas reductions. Testing was undertaken in the National Research Council Canada 2 m × 3 m Wind Tunnel with two 1/15-scale models of modern aerodynamic tractors paired with dry-van trailers configured with and without combinations of side-skirts and boat-tails. Separation distances of 0.14, 0.28, 0.49, 0.70 and 1.04 vehicle lengths were tested (3 m, 6 m, 10.5 m, 15 m, and 22.5 m full scale). Additionally, within-lane lateral offsets up to 0.31 vehicle widths (0.8 m full scale) were evaluated, along with a full-lane offset of 1.42 vehicle widths (3.7 m full scale). This study has made use of a wind-averaged-drag coefficient as the primary metric for evaluating the effect of vehicle platooning.

Abstract Connected vehicle technology consisting of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication falls under the umbrella of V2X, or Vehicle-to-Everything, communication. This enables vehicles and infrastructure to exchange safety-related information to enable smarter, safer roads. If driver alerts are raised or automated action is taken as a result of these messages, it is critical that messages are trustworthy and reliable. To this end, the Security Credential Management System (SCMS) and Cooperative Intelligent Transportation Systems (C-ITS) Credential Management System (CCMS) have been proposed to enable authentication and authorization of V2X messages without compromising individual user privacy. This is accomplished by issuing each vehicle a large set of “pseudonyms,” unrelated to any real-world identity. During operation, the vehicle periodically switches pseudonyms, thereby changing its identity to others in the network.

Abstract This article describes the application of Reinforcement Learning (RL) with an embedded heuristic algorithm to a multi-objective hybrid vehicle optimization. A multi-objective optimization problem (MOP) is defined as a minimization of total energy consumption and trip time resulting from optimal control of vehicle speed over a known route. First, a computationally efficient heuristic optimization algorithm is formulated to solve the MOP for multiple traffic scenarios. Then, the off-line integration of RL is applied to the heuristic optimization algorithm process and utilized to solve the MOP. Finally, the online optimization capability of the machine learning algorithm is discussed, as well as its extension to the vehicle routing problem and the hybrid electric vehicle. The specific scenario investigated is where a generic vehicle begins a trip on a one-lane highway. The length of the highway and the number of vehicles and traffic signals on the road are generic as well.

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 This work was supported jointly by the National Science Foundation of China under Grant No. 51875184 and the National key R&D programs, China New energy vehicles focus on special projects under Grant No. 2016YFB0100903-2.

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 Numerous operational procedures regulate aerodrome ground traffic. Detailed solutions in these procedures often come from preventive recommendations formulated as a result of accident cause analysis. With time, the conclusions drawn based on incidents, i.e., events that did not result in material damage or casualties, are becoming increasingly significant. In this article, we propose a new method for determining the probability of an incident turning into an air accident, based on the example of aerodrome traffic operations. Premises conducive to an accident in the considered class of events depend on both human and physical factors. Thus a hybrid approach was applied. We used a fuzzy inference system to analyze the premises dependent on vehicle operators, while the simulation method was selected to examine the premises dependent on physical factors. Both were integrated using the technique of event trees with fuzzy probabilities (ETFP).

Abstract The development of the technology of automated highways promises the opportunity for the vehicles to travel safely at a closer distance concerning each other. As such, vehicles moving in the wake of others experience a reduction in fuel consumption. This article investigates the effect of longitudinal distance between two passenger cars on drag coefficients numerically and experimentally. For the numerical analysis, the fluid flow at car speeds of 70, 90 and 110 km/h were examined. The Artificial Intelligence coding was applied to train an Artificial Neural Network to extend the calculated data. The optimum values for the inter-vehicle distance and the vehicle speed to assure the least drag coefficient are obtained. To support the numerical results an instrument designed and built particularly to accurately measure the fuel consumption was installed on a midsize sedan car and some field tests were carried out.

Abstract Road surface characteristics directly influence vehicle safety and performance, and its knowledge can be instrumental to road transportation system safety. This work focuses on the development of a test setup, which was utilized for real-time implementation of a road surface identification algorithm based on the acceleration response of an intelligent tire. Analysis of frequency domain data was used to leverage the tire-road contact information being relayed through the acceleration data. A signal processing algorithm was developed to separate each tire revolution, analyze it in real time, and convert it to the frequency domain in real time. In the end, the performance of the setup was validated with results from the literature, and the distinguishing signature possessed by each surface was used to categorize different terrains into the respective surface categories (Dry Asphalt, Wet Asphalt, Concrete) in real time.

Abstract For safe and comfortable automated driving in the urban domain, especially in complex geometries as intersections, the prediction of surrounding traffic participants is fundamental. Several works in this field focus on predicting the behavior of vulnerable road users (VRU) at crossings. However, no approaches were found dealing with predicting the interaction between turning vehicles giving right of way or cooperating with VRU, which is substantial for the trajectory planning of following vehicles. Infrastructural sensor data from an intersection in Germany enables the development of a prediction concept for vehicles interacting with VRU. Our studies show that the original criteria for classifying an interaction between vehicles and VRU—the post-encroachment time (PET)—is not suitable as ground truth criteria for the aimed prediction. Instead, a clustering-based labelling approach with k-means shows promising results in trajectory pattern distinction.

Abstract As of today, multiple studies suggest a perceptible influence of the vehicle body stiffness on the drivability and steering feel. Most of them use subjective methods to score changes in stiffness but do not conduct further measurements to explain the underlying physical chain. This interaction between the body stiffness and vehicle dynamics is not fully understood and requires further research, especially in the on-center behavior and maneuvers of low-lateral dynamics. This research focuses on these two areas by measuring the steering inputs, the resulting vehicle response and the vibrational behavior of the body on a freeway and a comfort test track. Afterward, the main effects of different stiffening measures are analyzed and discussed. Regarding the influence on the steering feel, differences can be measured but seem too small to be perceptible for a normal driver.

Abstract A classical antiskid brake system (ABS) is typically used to control the brake fluid pressure by creating repeated cycles of decreasing and increasing brake force to avoid wheel locking, causing the fluctuation of the brake hydraulic pressure and resulting in vibration during wheel rotation. This article proposes a new approach of skid control for ABS by controlling the disk pad position. This new approach involves using a modest control method to determine the optimal skid that allows the wheel to exert maximum friction force for decelerating the vehicle by shifting the brake pad position instead of modulating the brake fluid pressure. This pad position control (PPC) method works in a continuous manner. Therefore, no rapid changes are required in the brake pressure and wheel rotation speed. To identify the PPC braking performance, braking test simulations and experiments have been carried out.

Abstract TOC

Abstract Over the years, vehicular communication has become a significant and emerging research area. However, the vehicular platooning concept has gained importance only in the last couple of years or so. Typically, a platoon is a group of vehicles governed by the front vehicle, where all other vehicles follow the instruction of the lead vehicle. The vehicles in a platoon have the flexibility to leave the connected path when their destination has arrived. However, maintaining a continuous communication mechanism is a major challenge that still needs to be solved. In a high-speed dynamic setting of platooning, vehicles need not only to exactly follow the pattern of movement of the lead vehicle but also have seamless communication between the vehicles, even while entering and exiting the platoon. Particularly, multi-hop communication is a critical component for vehicular platooning wherein the lead vehicle could communicate with all vehicles in the platoon.

Abstract We survey the state of the art in off-board diagnostics for vehicles, their occupants, and environments, with particular focus on vibroacoustic (VA) approaches. We identify promising application areas including data-driven management for shared mobility and automated fleets, usage-based insurance, and vehicle, occupant, and environmental state and condition monitoring. We close by exploring the particular application of VA monitoring to vehicle diagnostics and prognostics and propose the introduction of automated vehicle- and context-specific model selection as a means of improving algorithm performance, e.g., to enable smartphone-resident diagnostics. Towards this vision, four strong-performing, interdependent classifiers are presented as a proof of concept for identifying vehicle configuration from acoustic signatures. The described approach may serve as the first step in developing “universal diagnostics,” with applicability extending beyond the automotive domain.

Abstract Autonomy and connectivity are considered among the most promising technologies to improve safety and mobility and reduce fuel consumption and travel delay in transportation systems. In this paper, we devise an optimal control-based trajectory planning model that can provide safe and efficient trajectories for the subject vehicle while incorporating platoon formation and lane-changing decisions. We embed this trajectory planning model in a simulation framework to quantify its fuel efficiency and travel time reduction benefits for the subject vehicle in a dynamic traffic environment. Specifically, we compare and analyze the statistical performance of different controller designs in which lane changing or platooning may be enabled, under different values of time (VoTs) for travelers.

Abstract The article considers the issues of a systemic approach to studying safety levels in transport operations and ways to increase the safety of the operator-machine system in Russian transport. The principal and problematic issues of reducing the risk of injury by preventing traffic accidents and reducing the severity of their impact have not been sufficiently addressed. When performing transport operations, there are often disagreements between the elements of the “Operator, Machine, and Environment” technological system due to the influence of external conditions and parameters of the constantly-changing environment in the workplace. This leads to a sharp increase in the number of failures of system elements, which reduces the level of safety of transport operations.

Abstract The conception of Automated Driving Systems is expanding fast with the expectation of the whole society and with heavy investments toward research and development. However, the insertion of these vehicles in real scenarios worldwide is still a challenge for governments, once they require an important evolution of the legal and regulatory framework. Although there are several initiatives to accelerate the insertion process, each country has specificities when considering the traffic scenario. In order to contribute to this emerging problem, this article presents a perspective of how the insertion of these vehicles can be performed considering specificities of the Brazilian scenario, one of the world's biggest car markets. Thus, it is discussed the global scenario of autonomous vehicles, the Brazilian traffic system, and the certification and homologation process, focusing on a new test track proposal.