Fiber-optic interferometric sensor for monitoring automobile and rail traffic

This article describes a fiber-optic interferometric sensor and measuring scheme including input-output components for traffic density monitoring. The proposed measuring system is based on the interference in optical fibers. The sensor, based on the Mach-Zehnder interferometer, is constructed to detect vibration and acoustic responses caused by vehicles moving around the sensor. The presented solution is based on the use of single-mode optical fibers (G.652.D and G.653) with wavelength of 1550 nm and laser source with output power of 1 mW. The benefit of this solution lies in electromagnetic interference immunity and simple implementation because the sensor does not need to be installed destructively into the roadway and railroad tracks. The measuring system was tested in real traffic and is characterized by detection success of 99.27% in the case of automotive traffic and 100% in the case of rail traffic.Web of Science2662995298

Satellite Navigation for the Age of Autonomy

Global Navigation Satellite Systems (GNSS) brought navigation to the masses. Coupled with smartphones, the blue dot in the palm of our hands has forever changed the way we interact with the world. Looking forward, cyber-physical systems such as self-driving cars and aerial mobility are pushing the limits of what localization technologies including GNSS can provide. This autonomous revolution requires a solution that supports safety-critical operation, centimeter positioning, and cyber-security for millions of users. To meet these demands, we propose a navigation service from Low Earth Orbiting (LEO) satellites which deliver precision in-part through faster motion, higher power signals for added robustness to interference, constellation autonomous integrity monitoring for integrity, and encryption / authentication for resistance to spoofing attacks. This paradigm is enabled by the 'New Space' movement, where highly capable satellites and components are now built on assembly lines and launch costs have decreased by more than tenfold. Such a ubiquitous positioning service enables a consistent and secure standard where trustworthy information can be validated and shared, extending the electronic horizon from sensor line of sight to an entire city. This enables the situational awareness needed for true safe operation to support autonomy at scale.Comment: 11 pages, 8 figures, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS

Managed information gathering and fusion for transient transport problems

This paper deals with vehicular traffic management by communication technologies from Traffic Control Center point of view in road networks. The global goal is to manage the urban traffic by road traffic operations, controlling and interventional possibilities in order to minimize the traffic delays and stops and to improve traffic safety on the roads. This paper focuses on transient transport, when the controlling management is crucial. The aim was to detect the beginning time of the transient traffic on the roads, to gather the most appropriate data and to get reliable information for interventional suggestions. More reliable information can be created by information fusion, several fusion techniques are expounded in this paper. A half-automatic solution with Decision Support System has been developed to help with engineers in suggestions of interventions based on real time traffic data. The information fusion has benefits for Decision Support System: the complementary sensors may fill the gaps of one another, the system is able to detect the changing of the percentage of different vehicle types in traffic. An example of detection and interventional suggestion about transient traffic on transport networks of a little town is presented at the end of the paper. The novelty of this paper is the gathering of information - triggered by the state changing from stationer to transient - from ad hoc channels and combining them with information from developed regular channels. --information gathering,information fusion,Kalman filter,transient traffic,Decision Support System

Smartphone-based vehicle telematics: a ten-year anniversary

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

Visual computing techniques for automated LIDAR annotation with application to intelligent transport systems

106 p.The concept of Intelligent Transport Systems (ITS) refers to the application of communication and information technologies to transport with the aim of making it more efficient, sustainable, and safer. Computer vision is increasingly being used for ITS applications, such as infrastructure management or advanced driver-assistance systems. The latest progress in computer vision, thanks to the Deep Learning techniques, and the race for autonomous vehicle, have created a growing requirement for annotated data in the automotive industry. The data to be annotated is composed by images captured by the cameras of the vehicles and LIDAR data in the form of point clouds. LIDAR sensors are used for tasks such as object detection and localization. The capacity of LIDAR sensors to identify objects at long distances and to provide estimations of their distance make them very appealing sensors for autonomous driving.This thesis presents a method to automate the annotation of lane markings with LIDAR data. The state of the art of lane markings detection based on LIDAR data is reviewed and a novel method is presented. The precision of the method is evaluated against manually annotated data. Its usefulness is also evaluated, measuring the reduction of the required time to annotate new data thanks to the automatically generated pre-annotations. Finally, the conclusions of this thesis and possible future research lines are presented

Design of an Automotive IoT Device to Improve Driver Fault Detection Through Road Class Estimation

Unsafe driver habits pose a serious threat to all vehicles on the road. This thesis outlines the development of an automotive IoT device capable of monitoring and reporting adverse driver habits to mitigate the occurrence of unsafe practices. The driver habits targeted are harsh braking, harsh acceleration, harsh cornering, speeding and over revving the vehicle. With the intention of evaluating and expanding upon the industry method of fault detection, a working prototype is designed to handle initialization, data collection, vehicle state tracking, fault detection and communication. A method of decoding the broadcasted messages on the vehicle bus is presented and unsafe driver habits are detected using static limits. An analysis of the initial design’s performance revealed that the industry method of detecting faults fails to account for the vehicle’s speed and is unable to detect faults on all roadways. A framework for analyzing fault profiles at varying speeds is presented and yields the relationship between fault magnitude and speed. A method of detecting the type of road driven was developed to dynamically assign fault limits while the vehicle traveled on a highway, city street or in traffic. The improved design correctly detected faults along all types of roads and proved to greatly expand upon the current method of fault detection used by the automotive IoT industry today