Optimisation of Rail-road Level Crossing Closing Time in a Heterogenous Railway Traffic: Towards Safety Improvement - South African Case Study

The gravitation towards mobility-as-a service in railway transportation system can be achieved at low cost and effort using shared railway network. However, the problem with shared networks is the presence of the level crossings where railway and road traffic intersects. Thus, long waiting time is expected at the level crossings due to the increase in traffic volume and heterogeneity. Furthermore, safety and capacity can be severely compromised by long level crossing closing time. The emphasis of this study is to optimise the rail-road level crossing closing time in order to achieve improved safety and capacity in a heterogeneous railway network. It is imperative to note that rail-road level crossing system assumes the socio-technical and safety critical duality which often impedes improvement efforts. Therefore, thorough understanding of the factors with highest influence on the level crossing closing time is required. Henceforth, data analysis has been conducted on eight active rail-road level crossings found on the southern corridor of the Western Cape metro rail. The spatial, temporal and behavioural analysis was conducted to extract features with influence on the level crossing closing time. Convex optimisation with the objective to minimise the level crossing closing time is formulated taking into account identified features. Moreover, the objective function is constrained by the train's traction characteristics along the constituent segments of the rail-road level crossing, speed restriction and headway time. The results show that developed solution guarantees at most 53.2% and 62.46% reduction in the level crossing closing time for the zero and nonzero dwell time, respectively. Moreover, the correctness of the presented solution has been validated based on the time lost at the level crossing and railway traffic capacity consumption. Thus, presented solution has been proven to achieve at most 50% recovery of the time lost per train trip and at least 15% improvement in capacity under normal conditions. Additionally, 27% capacity improvement is achievable at peak times and can increase depending on the severity of the headway constraints. However, convex optimisation of the level crossing closing time still fall short in level crossing with nonzero dwell time due to the approximation of dwell time based on the anticipated rather than actual value

The low-level guidance of an experimental autonomous vehicle

This thesis describes the data processing and the control that constitutes a method of guidance for an autonomous guided vehicle (AGV) operating in a predefined and structured environment such as a warehouse or factory. A simple battery driven vehicle has been constructed which houses an MC68000 based microcomputer and a number of electronic interface cards. In order to provide a user interface, and in order to integrate the various aspects of the proposed guidance method, a modular software package has been developed. This, along with the research vehicle, has been used to support an experimental approach to the research. The vehicle's guidance method requires a series of concatenated curved and straight imaginary Unes to be passed to the vehicle as a representation of a planned path within its environment. Global position specifications for each line and the associated AGV direction and demand speed for each fine constitute commands which are queued and executed in sequence. In order to execute commands, the AGV is equipped with low level sensors (ultrasonic transducers and optical shaft encoders) which allow it to estimate and correct its global position continually. In addition to a queue of commands, the AGV also has a pre-programmed knowledge of the position of a number of correction boards within its environment. These are simply wooden boards approximately 25cm high and between 2 and 5 metres long with small protrusions ("notches") 4cm deep and 10cm long at regular (Im) intervals along its length. When the AGV passes such a correction board, it can measure its perpendicular distance and orientation relative to that board using two sets of its ultrasonic sensors, one set at the rear of the vehicle near to the drive wheels and one set at the front of the vehicle. Data collected as the vehicle moves parallel to a correction board is digitally filtered and subsequently a least squares line fitting procedure is adopted. As well as improving the reliability and accuracy of orientation and distance measurements relative to the board, this provides the basis for an algorithm with which to detect and measure the position of the protrusions on the correction board. Since measurements in three planar, local coordinates can be made (these are: x, the distance travelled parallel to a correction board; and y,the perpendicular distance relative to a correction board; and Ɵ, the clockwise planar orientation relative to the correction board), global position estimation can be corrected. When position corrections are made, it can be seen that they appear as step disturbances to the control system. This control system has been designed to allow the vehicle to move back onto its imaginary line after a position correction in a critically damped fashion and, in the steady state, to track both linear and curved command segments with minimum error

Laser-Based Detection and Tracking of Moving Obstacles to Improve Perception of Unmanned Ground Vehicles

El objetivo de esta tesis es desarrollar un sistema que mejore la etapa de percepción de vehículos terrestres no tripulados (UGVs) heterogéneos, consiguiendo con ello una navegación robusta en términos de seguridad y ahorro energético en diferentes entornos reales, tanto interiores como exteriores. La percepción debe tratar con obstáculos estáticos y dinámicos empleando sensores heterogéneos, tales como, odometría, sensor de distancia láser (LIDAR), unidad de medida inercial (IMU) y sistema de posicionamiento global (GPS), para obtener la información del entorno con la precisión más alta, permitiendo mejorar las etapas de planificación y evitación de obstáculos. Para conseguir este objetivo, se propone una etapa de mapeado de obstáculos dinámicos (DOMap) que contiene la información de los obstáculos estáticos y dinámicos. La propuesta se basa en una extensión del filtro de ocupación bayesiana (BOF) incluyendo velocidades no discretizadas. La detección de velocidades se obtiene con Flujo Óptico sobre una rejilla de medidas LIDAR discretizadas. Además, se gestionan las oclusiones entre obstáculos y se añade una etapa de seguimiento multi-hipótesis, mejorando la robustez de la propuesta (iDOMap). La propuesta ha sido probada en entornos simulados y reales con diferentes plataformas robóticas, incluyendo plataformas comerciales y la plataforma (PROPINA) desarrollada en esta tesis para mejorar la colaboración entre equipos de humanos y robots dentro del proyecto ABSYNTHE. Finalmente, se han propuesto métodos para calibrar la posición del LIDAR y mejorar la odometría con una IMU

Experimental Testbed for Swarming and Cooperative Robotic Networks

This document describes an innovative cooperative robotics multi-vehicle testbed, featuring a flexible architecture that enables the system to be rapidly adapted to different applications. It also offers tools to reduce development and implementation time. The testbed consists of ten non-holonomic car-like robots networked together to share sensor information. Each vehicle features an on-board computer for local control, and a network of devices that can be suited with a variety of hot-swappable sensors depending on the application. The entire system is integrated with Player, an open source sensor server compatible with Gazebo, a 3D world simulator. Control algorithms can be evaluated in simulation mode and then ported to the real vehicle with virtually no code change. We present a flexible and complete system that serves the study of Cooperative Control, Hybrid and Embedded Systems, Sensor Networks, Networked Control and that can be used in an extensive range of applications.School of Electrical & Computer Engineerin

Analysis of SHRP2 Data to Understand Normal and Abnormal Driving Behavior in Work Zones

This research project used the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study(NDS) to improve highway safety by using statistical descriptions of normal driving behavior to identify abnormal driving behaviors in work zones. SHRP2 data used in these analyses included 50 safety-critical events (SCEs) from work zones and 444 baseline events selected on a matched case-control design.Principal components analysis (PCA) was used to summarize kinematic data into “normal” and “abnormal”driving. Each second of driving is described by one point in three-dimensional principal component (PC) space;an ellipse containing the bulk of baseline points is considered “normal” driving. Driving segments without-of-ellipse points have a higher probability of being an SCE. Matched case-control analysis indicates that thespecific individual and traffic flow made approximately equal contributions to predicting out-of-ellipse driving.Structural Topics Modeling (STM) was used to analyze complex categorical data obtained from annotated videos.The STM method finds “words” representing categorical data variables that occur together in many events and describes these associations as “topics.” STM then associates topics with either baselines or SCEs. The STM produced 10 topics: 3 associated with SCEs, 5 associated with baselines, and 2 that were neutral. Distractionoccurs in both baselines and SCEs.Both approaches identify the role of individual drivers in producing situations where SCEs might arise. A countermeasure could use the PC calculation to indicate impending issues or specific drivers who may havehigher crash risk, but not to employ significant interventions such as automatically braking a vehicle without-of-ellipse driving patterns. STM results suggest communication to drivers or placing compliant vehicles in thetraffic stream would be effective. Finally, driver distraction in work zones should be discouraged

VISION-BASED URBAN NAVIGATION PROCEDURES FOR VERBALLY INSTRUCTED ROBOTS

The work presented in this thesis is part of a project in instruction based learning (IBL) for mobile robots were a robot is designed that can be instructed by its users through unconstrained natural language. The robot uses vision guidance to follow route instructions in a miniature town model. The aim of the work presented here was to determine the functional vocabulary of the robot in the form of "primitive procedures". In contrast to previous work in the field of instructable robots this was done following a "user-centred" approach were the main concern was to create primitive procedures that can be directly associated with natural language instructions. To achieve this, a corpus of human-to-human natural language instructions was collected and analysed. A set of primitive actions was found with which the collected corpus could be represented. These primitive actions were then implemented as robot-executable procedures. Natural language instructions are under-specified when destined to be executed by a robot. This is because instructors omit information that they consider as "commonsense" and rely on the listener's sensory-motor capabilities to determine the details of the task execution. In this thesis the under-specification problem is solved by determining the missing information, either during the learning of new routes or during their execution by the robot. During learning, the missing information is determined by imitating the commonsense approach human listeners take to achieve the same purpose. During execution, missing information, such as the location of road layout features mentioned in route instructions, is determined from the robot's view by using image template matching. The original contribution of this thesis, in both these methods, lies in the fact that they are driven by the natural language examples found in the corpus collected for the IDL project. During the testing phase a high success rate of primitive calls, when these were considered individually, showed that the under-specification problem has overall been solved. A novel method for testing the primitive procedures, as part of complete route descriptions, is also proposed in this thesis. This was done by comparing the performance of human subjects when driving the robot, following route descriptions, with the performance of the robot when executing the same route descriptions. The results obtained from this comparison clearly indicated where errors occur from the time when a human speaker gives a route description to the time when the task is executed by a human listener or by the robot. Finally, a software speed controller is proposed in this thesis in order to control the wheel speeds of the robot used in this project. The controller employs PI (Proportional and Integral) and PID (Proportional, Integral and Differential) control and provides a good alternative to expensive hardware

Simultaneous Trajectory Estimation and Mapping for Autonomous Underwater Proximity Operations

Due to the challenges regarding the limits of their endurance and autonomous capabilities, underwater docking for autonomous underwater vehicles (AUVs) has become a topic of interest for many academic and commercial applications. Herein, we take on the problem of state estimation during an autonomous underwater docking mission. Docking operations typically involve only two actors, a chaser and a target. We leverage the similarities to proximity operations (prox-ops) from spacecraft robotic missions to frame the diverse docking scenarios with a set of phases the chaser undergoes on the way to its target. We use factor graphs to generalize the underlying estimation problem for arbitrary underwater prox-ops. To showcase our framework, we use this factor graph approach to model an underwater homing scenario with an active target as a Simultaneous Localization and Mapping problem. Using basic AUV navigation sensors, relative Ultra-short Baseline measurements, and the assumption of constant dynamics for the target, we derive factors that constrain the chaser's state and the position and trajectory of the target. We detail our front- and back-end software implementation using open-source software and libraries, and verify its performance with both simulated and field experiments. Obtained results show an overall increase in performance against the unprocessed measurements, regardless of the presence of an adversarial target whose dynamics void the modeled assumptions. However, challenges with unmodeled noise parameters and stringent target motion assumptions shed light on limitations that must be addressed to enhance the accuracy and consistency of the proposed approach.Comment: 19 pages, 14 figures, submitted to the IEEE Journal of Oceanic Engineerin

Monitoring the driver's activity using 3D information

Driver supervision is crucial in safety systems for the driver. It is important to monitor the driver to understand his necessities, patterns of movements and behaviour under determined circumstances. The availability of an accurate tool to supervise the driver’s behaviour allows multiple objectives to be achieved such as the detection of drowsiness (analysing the head movements and blinking pattern) and distraction (estimating where the driver is looking by studying the head and eyes position). Once the misbehaviour is detected in both cases an alarm, of the correct type according to the situation, could be triggered to correct the driver’s behaviour. This application distinguishes itself form other driving assistance systems due to the fact that it is oriented to analyse the inside of the vehicle instead of the outside. It is important to notice that inside supervising applications are as important as the outside supervising applications because if the driver falls asleep, a pedestrian detection algorithm can do only limited actions to prevent the accident. All this under the best and predetermined circumstances. The application has the potential to be used to estimate if the driver is looking at certain area where another application detected that an obstacle is present (inert object, animal or pedestrian). Although the market has already available technologies, able to provide automatic driver monitoring, the associated cost of the sensors to accomplish this task is very high as it is not a popular product (compared to other home or entertaining devices) nor there is a market with a high demand and supply for this sensors. Many of these technologies require external and invasive devices (attach one or a set of sensors to the body) which may interfere the driving movements proper of the nature of the driver under no supervised conditions. Current applications based on computer vision take advantage of the latest development of information technologies and the increase in computational power to create applications that fit to the criteria of a non-invasive method for driving monitoring application. Technologies such as stereo and time of flight cameras are able to overcome some of the difficulties related to computer vision applications such as extreme lighting conditions (too dark or too bright) saturation of the colour sensors and lack of depth information. It is true that the combination of different sensors can overcome this problems by performing multiple scans from different areas or by combining the information obtained from different devices but this requires an additional step of calibration, positioning and it involves a dependability factor of the application on not one but as many sensors included in the task to perform the supervision because if one of them fails, the results may not be correct. Some of the recent gaming sensors available in the market, such as the Kinect sensor bar form Microsoft, are providing a new set of previously-expensive sensors embedded in a low cost device, thus providing 3D information together with some additional features and without the need for complex sets of handcrafted system that can fail as previously mentioned. The proposed solution in this thesis monitors the driver by using the different data from the Kinect sensor (depth information, infrared and colour image). The fusion of the information from the different sources allows the usage of 2D and 3D algorithms in order to provide a reliable face detection, accurate pose estimation and trustable detection of facial features such as the eyes and nose. The system will compare, with an average speed over 10Hz, the initial face capture with the next frames, it will compare by an iterative algorithm previously configured with the compromise of accuracy and speed. In order to determine the reliability and accuracy of the proposed system, several tests were performed for the head-pose orientation algorithm with an Inertial Measurement Unit (IMU) attached to the back of the head of the collaborative subjects. The inertial measurements provided by the IMU were used as a ground truth for three degrees of freedom (3DoF) tests (yaw, pitch and roll). Finally, the tests results were compared with those available in current literature to check the performance of the algorithm presented. Estimating the head orientation is the main function of this proposal as it is the one that delivers more information to estimate the behaviour of the driver. Whether it is to have a first estimation if the driver is looking to the front or if it is presenting signs of fatigue when nodding. Supporting this tool, is another that is in charge of the analysis of the colour image that will deal with the study of the eyes of the driver. From this study, it will be possible to estimate where the driver is looking at by estimating the gaze orientation through the position of the pupil. The gaze orientation would help, along with the head orientation, to have a more accurate guess regarding where the driver is looking. The gaze orientation is then a support tool that complements the head orientation. Another way to estimate a hazardous situation is with the analysis of the opening of the eyes. It can be estimated if the driver is tired through the study of the driver’s blinking pattern during a determined time. If it is so, the driver increases the chance to cause an accident due to drowsiness. The part of the whole solution that deals with solving this problem will analyse one eye of the driver to estimate if it is closed or open according to the analysis of dark regions in the image. Once the state of the eye is determined, an analysis during a determined period of time will be done in order to know if the eye was most of the time closed or open and thus estimate in a more accurate way if the driver is falling asleep or not. This 2 modules, drowsiness detector and gaze estimator, will complement the estimation of the head orientation with the goal of getting more certainty regarding the driver’s status and, when possible, to prevent an accident due to misbehaviours. It is worth to mention that the Kinect sensor is built specifically for indoor use and connected to a video console, not for the outside. Therefore, it is inevitable that some limitations arise when performing monitoring under real driving conditions. They will be discussed in this proposal. However, the algorithm presented can be used with any point-cloud based sensor (stereo cameras, time of flight cameras, laser scanners etc...); more expensive, but less sensitive compared to the former. Future works are described at the end in order to show the scalability of this proposal.La supervisión del conductor es crucial en los sistemas de asistencia a la conducción. Resulta importante monitorizarle para entender sus necesidades, patrones de movimiento y comportamiento bajo determinadas circunstancias. La disponibilidad de una herramienta precisa que supervise el comportamiento del conductor permite que varios objetivos sean alcanzados como la detección de somnolencia (analizando los movimientos de la cabeza y parpadeo) y distracción (estimando hacia donde está mirando por medio del estudio de la posición tanto de la cabeza como de los ojos). En ambos casos, una vez detectado el mal comportamiento, se podría activar una alarma del tipo adecuado según la situación que le corresponde con el objetivo de corregir su comportamiento del conductor Esta aplicación se distingue de otros sistemas avanzados de asistencia la conducción debido al hecho de que está orientada al análisis interior del vehículo en lugar del exterior. Es importante notar que las aplicaciones de supervisión interna son tan importantes como las del exterior debido a que si el conductor se duerme, un sistema de detección de peatones o vehículos sólo podrá hacer ciertas maniobras para evitar un accidente. Todo esto bajo las condiciones idóneas y circunstancias predeterminadas. Esta aplicación tiene el potencial para estimar si quien conduce está mirando hacia una zona específica que otra aplicación que detecta objetos, animales y peatones ha remarcado como importante. Aunque en el mercado existen tecnologías disponibles capaces de supervisar al conductor, estas tienen un coste prohibitivo para cierto grupo de clientela debido a que no es un producto popular (comparado con otros dispositivos para el hogar o de entretenimiento) ni existe un mercado con alta oferta y demanda de dichos dispositivos. Muchas de estas tecnologías requieren de dispositivos externos e invasivos (colocarle al conductor uno o más sensores en el cuerpo) que podrían interferir con la naturaleza de los movimientos propios de la conducción bajo condiciones sin supervisar. Las aplicaciones actuales basadas en visión por computador toman ventaja de los últimos desarrollos de la tecnología informática y el incremento en poder computacional para crear aplicaciones que se ajustan al criterio de un método no invasivo para aplicarlo a la supervisión del conductor. Tecnologías como cámaras estéreo y del tipo “tiempo de vuelo” son capaces de sobrepasar algunas de las dificultades relacionadas a las aplicaciones de visión por computador como condiciones extremas de iluminación (diurna y nocturna), saturación de los sensores de color y la falta de información de profundidad. Es cierto que la combinación y fusión de sensores puede resolver este problema por medio de múltiples escaneos de diferentes zonas o combinando la información obtenida de diversos dispositivos pero esto requeriría un paso adicional de calibración, posicionamiento e involucra un factor de dependencia de la aplicación hacia no uno sino los múltiples sensores involucrados ya que si uno de ellos falla, los resultados podrían no ser correctos. Recientemente han aparecido en el mercado de los videojuego algunos sensores, como es el caso de la barra de sensores Kinect de Microsoft, dispositivo de bajo coste, que ofrece información 3D junto con otras características adicionales y sin la necesidad de sistemas complejos de sistemas manufacturados que pueden fallar como se ha mencionado anteriormente. La solución propuesta en esta tesis supervisa al conductor por medio del uso de información diversa del sensor Kinect (información de profundidad, imágenes de color en espectro visible y en espectro infrarrojo). La fusión de información de diversas fuentes permite el uso de algoritmos en 2D y 3D con el objetivo de proveer una detección facial confiable, estimación de postura precisa y detección de características faciales como los ojos y la nariz. El sistema comparará, con una velocidad promedio superior a 10Hz, la captura inicial de la cara con el resto de las imágenes de video, la comparación la hará por medio de un algoritmo iterativo previamente configurado comprometido con el balance entre velocidad y precisión. Con tal de determinar la fiabilidad y precisión del sistema propuesto, diversas pruebas fueron realizadas para el algoritmo de estimación de postura de la cabeza con una unidad de medidas inerciales (IMU por sus siglas en inglés) situada en la parte trasera de la cabeza de los sujetos que participaron en los ensayos. Las medidas inerciales provistas por la IMU fueron usadas como punto de referencia para las pruebas de los tres grados de libertad de movimiento. Finalmente, los resultados de las pruebas fueron comparados con aquellos disponibles en la literatura actual para comprobar el rendimiento del algoritmo aquí presentado. Estimar la orientación de la cabeza es la función principal de esta propuesta ya que es la que más aporta información para la estimación del comportamiento del conductor. Sea para tener una primera estimación si ve hacia el frente o si presenta señales de fatiga al cabecear hacia abajo. Acompañando a esta herramienta, está el análisis de la imagen a color que se encargará del estudio de los ojos. A partir de dicho estudio, se podrá estimar hacia donde está viendo el conductor según la posición de la pupila. La orientación de la mirada ayudaría, junto con la orientación de la cabeza, a saber hacia dónde ve el conductor. La estimación de la orientación de la mirada es una herramienta de soporte que complementa la orientación de la cabeza. Otra forma de determinar una situación de riesgo es con el análisis de la apertura de los ojos. A través del estudio del patrón de parpadeo en el conductor durante un determinado tiempo se puede estimar si se encuentra cansado. De ser así, el conductor aumenta las posibilidades de causar un accidente debido a la somnolencia. La parte de la solución que se encarga de resolver este problema analizará un ojo del conductor para estimar si se encuentra cerrado o abierto de acuerdo al análisis de regiones de interés en la imagen. Una vez determinado el estado del ojo, se procederá a hacer un análisis durante un determinado tiempo para saber si el ojo ha estado mayormente cerrado o abierto y estimar de forma más acertada si se está quedando dormido o no. Estos 2 módulos, el detector de somnolencia y el análisis de la mirada complementarán la estimación de la orientación de la cabeza con el objetivo de brindar mayor certeza acerca del estado del conductor y, de ser posible, prevenir un accidente debido a malos comportamientos. Es importante mencionar que el sensor Kinect está construido específicamente para el uso dentro de una habitación y conectado a una videoconsola, no para el exterior. Por lo tanto, es inevitable que algunas limitaciones salgan a luz cuando se realice la monitorización bajo condiciones reales de conducción. Dichos problemas serán mencionados en esta propuesta. Sin embargo, el algoritmo presentado es generalizable a cualquier sensor basado en nubes de puntos (cámaras estéreo, cámaras del tipo “time of flight”, escáneres láseres etc...); más caros pero menos sensibles a estos inconvenientes previamente descritos. Se mencionan también trabajos futuros al final con el objetivo de enseñar la escalabilidad de esta propuesta.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Andrés Iborra García.- Secretario: Francisco José Rodríguez Urbano.- Vocal: José Manuel Pastor Garcí

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system