Nowadays, the location technology in the field of the Intelligent Transformation System (ITS) is used generally. Most of location devices on the cars are low-cost GPS, however, it¡¦s not enough if we want to combine with the safe algorithm. Hence, we present a suit of vehicle collision-avoidance system which combined location technology with Intersection-agent in this thesis. The system uses vehicle sensors and GPS information to calculate in Extend Kalman Filter, in order to get the optimal location information. Furthermore, Map-Matching algorithm is used to match the vehicle location on the right road. As to the driver¡¦s safety, laser range scanner¡¦s data are used in fuzzy algorithm and calculate the safe distance between cars. In the intersection area where accident happened most, we also combine with Intersection-agent system to enhance safety. When moving objects cross through the intersection area, Intersection-agent system would use laser range scanner to find the moving objects¡¦ position and velocity, judging whether they can pass the intersection safely or not. Once it¡¦s not safe, system would send out warning signal to the drivers to brake cars, also, passing the position information to car location system by wireless RS-232 transceiver, to decrease location error and let vehicle¡¦s location precision more accurate. In brief, this thesis combines with vehicle location, wireless transmission, car following warning system and Intersection-agent. And make sure this system we developed can fit in with traffic requirement in many experiments.

Thesis (M.A.)--Naval Postgraduate School, Monterey, Calif., 2001. / "September 2001." Includes bibliographical references (p. 87-90). Available also online as a PDF file via the World Wide Web.

The potential benefits that automation could bring to a wide variety of real-world tasks are numerous and well recognised. There has been significant research undertaken into automation in general, but for real-time automation of complex systems (involving complex geometries and dynamics) the problem is far from a solved one. One of the key tasks in a surface mining operation is that of using shovels or excavators to load material onto haul trucks for transportation. Since it is such a crucial task to a number of production cycles, it is a clear area where the productivity and safety benefits of automation could have a large impact. A number of projects are being undertaken concurrently to move towards first partial, and then full, automation of this mining subsystem. This thesis focusses on the collision avoidance problem, specifically on forming a collision hull that distinguishes between intersecting and non-intersecting configurations of two objects. Techniques from computer graphics are leveraged to develop a data structure that stores and organises relevant information about real-world systems for motion-planning tasks, ensuring that the necessary data is available and in a form suited to the task at hand. The Minkowski Sum operation, which can be used fairly directly to form the collision hull of two convex objects under translation, is extended to develop an operation to form the exact collision hull of two arbitrary objects to determine the applicability of such a scheme to complex systems in real-time. A level of detail solution is then proposed, where the Minkowski Hull of bounding hierarchies allows unnecessary parts of the hull to be calculated only in a coarse manner, thus offsetting a lot of the computational cost for any given test. This approach is investigated for both translational motion and joint-space motion. Collision detection is not collision avoidance, and so the algorithms developed in the thesis are tested in a number of applications, to demonstrate their suitability to the collision avoidance task. The applications (discrete collision prediction, visibility graph path planning, and the formulation of a Model Predictive Controller) are restricted versions of the true problems with some simplifying assumptions, but they show the algorithms to be capable both in their execution speed and the information that they provide.

In this project, a novel human friendly mobile robot navigation controller is investigated. By applying this controller, the mobile robot is able to work in a complicated environment with several humans and other obstacles avoiding them before a collision happens. This robot will have a preference in avoiding humans over other obstacles keeping human safety as its first consideration. To achieve this goal, three problems have to be solved. The first one is the robot should be able to “see” the environment and distinguish the human and the obstacles. The functions of human sensor and sonar sensor are presented. A new sensor fusion method for combining the information collected by these two sorts of sensors based on Dempster-Shafer evidence theory is also proposed. By using the sensor fusion method, the robot will have a better view of human. The second problem is the robot has to know how to avoid collision. A new navigation algorithm, based on an improved velocity potential field method, is then described. The way of calculating the distances of avoidance based on different kinds of obstacles is presented as well. The last problem is how to make the mobile robot put human as its first priority when avoiding collision. A summary of the methods which are used to protect human is mentioned. According to the simulation and the experimental results, the new mobile robot navigation controller successfully led the robot avoid collisions in complicated situations and always put human safety as its first consideration.

Le processus de réalisation des pièces de formes complexes par usinage est un processus essentiel dans les domaines de l'aéronautique, de l'automobile, des moules et des matrices. Alors que l'usinage 5 axes grande vitesse est maintenant répandu dans les grands groupes industriels, il reste plusieurs problématiques à traiter. L'évitement de collisions le long de la trajectoire outil programmée en alors traité, notamment au niveau des interférences globales représentant une collision entre l'outil et son environnement. Classiquement, l'évitement de collisions dans le domaine de l'usinage 5 axes grande vitesse peut être programmé à l'aide d'une analyse géométrique de la situation. Si une collision est détecté, alors une phase de correction et d'optimisation peuvent être utilisée afin d'obtenir une nouvelle trajectoire hors collision. Le but des travaux est alors d'utiliser une modélisation physique afin d'obtenir une trajectoire corrigée hors collision le plus lisse possible. Pour ce faire le mouvement de l'outil est alors étudié d'un point de vue dynamique afin d'éviter les réorientation brutal post correction. De plus, les éléments constituants les obstacles émettent une action répulsive à distance. Cela permet, au cours de la programmation, d'anticiper l'approche d'un obstacle et ainsi d'entamer les corrections d'orientation outil en prévision d'une possible collision. Cette démarche de modélisation du mouvement étudiée permet alors de réaliser des simulations sur des pièces classiquement usinées dans les domaines énoncés précédemment. Dans le but de généraliser la programmation réalisée, il est alors important de comprendre comment les éléments obstacles sont représentés ainsi que la modélisation retenu pour l'outil utilisé au cours de la simulation. Enfin, la résolution de la dernière problématique mise en avant au cours de cette thèse concerne les temps de calcul obtenus. Il a été montré, après de multiples simulations, que ces derniers peuvent exploser d'un point de vue combinatoire pour des utilisateurs exigeants (modélisation fine de l'outil et de l'environnement). Une méthode de pré calcul est alors présentée utilisant la voxelisation permettant de diminuer les temps de calcul de manière très importante sans pour autant perdre de manière importante sur la solution obtenue. Le dernier objectif présenté est de proposer une approximation permettant de diminuer nettement les temps de calcul tout en conservant une assurance de non-collision. Cette méthode notée voxelisation consiste en utilisant une interpolation à diminuer le temps de calcul. L’important est alors de comprendre quels inconvénients se rattachent à la voxelisation et à partir de quand cette dernière apporte un résultat acceptable / Although 5-axis free form surfaces machining is commonly proposed in CAD/CAM software, several issues still need to be addressed and especially collision avoidance between the tool and the part. Indeed, advanced user skills are often required to define smooth tool axis orientations along the tool path in high speed machining. In the literature, the problem of collision avoidance is mainly treated as an iterative process based on local and geometrical collision tests. In this paper, an innovative method based on potential fields is used to generate 5-axis collision-free smooth tool paths. In the proposed approach, The ball-end tool is considered as a rigid body moving in 3D space on which repulsive force, deriving from a scalar potential field attached to the check surfaces, and attractive forces are acting. The resolution of the differential equations of the tool motion ensure smooth variations of the tool axis orientation. The proposed algorithm is applied on open pocket parts such as an impeller and a pocket corner to emphasize the effectiveness of this method to avoid collision. After that, it is possible to see that de calculation time can be very importante for a delicate mesh. It is for that, a voxelisation method is developed to decrease these.

Indiana University-Purdue University Indianapolis (IUPUI) / Autonomous vehicles currently dominate the automobile field for their impact on humanity and society. Connected and Automated Vehicles (CAV’s) are vehicles that use different communication technologies to communicate with other vehicles, infrastructure, the cloud, etc. With the information received from the sensors present, the vehicles analyze and take necessary steps for smooth, collision-free driving. This the sis talks about the cruise control system along with the intersection collision avoidance system based on Petri net models. It consists of two internal controllers for velocity and distance control, respectively, and three external ones for collision avoidance. Fault-tolerant redundant controllers are designed to keep these three controllers in check. The model is built using a PN toolbox and tested for various scenarios. The model is also validated, and its distinct properties are analyzed.

A dynamic modeling framework was established to predict the position and alignment (turning angle) of a tractor-dolly towing system receiving different driver inputs. This framework consists of three primary components: (1) a state space model to determine position and velocity of the system through Newton’s second law; (2) a model that transfers angular acceleration through each successive towed vehicle; and (3) a polygon model to draw an instantaneous shape of the vehicle representing its location and alignment. Input parameters of this model include initial conditions of the system, real time location of a reference point that can be determined through a beacon and radar system, and instantaneous accelerations, which come from driver maneuvers found on a data collecting system installed on the tractor. The purpose is to create an output that presents the position of the dolly vehicles with reference to the tractor at any time point.

Collision avoidance is an essential capability for autonomous and assisted-driving ground vehicles. In this work, we developed a novel model predictive control based intelligent collision avoidance (CA) algorithm for a multi-trailer industrial ground vehicle implemented on a General Purpose Graphical Processing Unit (GPGPU). The CA problem is formulated as a multi-objective optimal control problem and solved using a limited look-ahead control scheme in real-time. Through hardware-in-the-loop-simulations and experimental results obtained in this work, we have demonstrated that the proposed algorithm, using NVIDA’s CUDA framework and the NVIDIA Jetson TX2 development platform, is capable of dynamically assisting drivers and maintaining the vehicle a safe distance from the detected obstacles on-thely. We have demonstrated that a GPGPU, paired with an appropriate algorithm, can be the key enabler in relieving the computational burden that is commonly associated with model-based control problems and thus make them suitable for real-time applications.

This thesis investigates the problem of sensors used with mobile robots. Firstly, a colour sensor is considered, for its ability to detect objects having the three primary colours Red, Green and Blue (RGB). Secondly, an accelerometer was investigated, from which velocity was derived from the raw data using numerical integration. The purpose of the design and development of the sensors was to use them for robotic navigation and collision avoidance. This report presents the results of experiments carried out on the colour sensor and the accelerometer. A discussion of the results and some conclusions are also presented. It proved feasible to achieve the goal of detecting colours successfully but only for a limited distance. The accelerometer proved reliable but is not yet being applied in real time. Both the colour sensor and the accelerometer proved to be inexpensive. Some recommendations are made to improve both the colour sensor and the accelerometer sensors.

Highly Automated Vehicles (AVs) have the capability to revolutionize the transportation system. These systems have the possibility to make roads safer as AVs do not have limitations that human drivers do, many of which are common causes of vehicle crashes (e.g., distraction or fatigue) often defined generically as human error. The deployment of AVs is likely to be very gradual however, and there will exist situations in which the AV will be driving in close proximity with human drivers across the foreseeable future. Given the persistent crash problem in which the makority of crashes are attributed to driver error, humans will continue to create potential collision scenarios that an AV will be expected to try and avoid or mitigate if developed appropriately. The absence of unreasonable risk in an AVs ability to comprehend and react in these situations is referred to as operational safety. Unlike advanced driver assistance systems (ADAS), highly automated vehicles are required to perform the entirety of the dynamic driving task (DDT) and have a greater responsibility to achieve a high level of operational safety. To address this concern, scenario-based testing has increasingly become a popular option for evaluating AV performance. On a functional level, an AV typically consists of three basic systems: the perception system, the decision and path planning system, and vehicle motion control system. A minimum level of performance is needed in each of these functional blocks to achieve an adequate level of operational safety. The goal of this study was to investigate the effects that perception system performance (i.e., target object state errors) has on vehicle operational safety in collision scenarios similar to that created by human drivers. In the first part of this study, recent annual crash data was used to define a relevant crash population of possible scenarios involving intersections that an AV operating as an urban taxi may encounter. Common crash maneuvers and characteristics were combined to create a set of testing scenarios that represent a high iii percentage of the overall crash population. In the second part of this study, each test scenario was executed using an AV test platform during closed road testing to determine possible real-world perception system performance. This provided a measure of the error in object detection measurements compared to the ideal (i.e., where a vehicle was detected to be compared to where it actually was). In the third part of this study, a set of vehicle simulations were performed to assess the effect of perception system performance on crash outcomes. This analysis simulated hypothetical crashes between an AV and one other collision partner. First an initial worst-case impact configuration was defined and was based on injury outcomes seen in crash data. The AV was then simulated to perform a variety of evasive maneuvers based on an adaptation of a non-impaired driver model. The impact location and orientation of the collision partner was simulated as two states: one based on the object detection of an ideal perception system and the other based on the object detection of the perception system from the AV platform used during the road testing. For simulations in which the two vehicles contacted each other, a planar momentum-impulse model was used for impact modeling and injury outcomes were predicted using an omni-directional injury model taken from recent literature. Results from this study indicate that errors in perception system measurements can change the perceived occupant injury risk within a crash. Sensitivity was found to be dependent on the specific crash type as well as what evasive maneuver is taken. Sensitivities occurred mainly due to changes in the principal direction of force for the crash and the interaction within the injury risk prediction curves. In order to achieve full operational safety, it will likely be important to understand the influence that each functional system (perception, decision, and control) may have on AV performance in these crash scenarios. / Master of Science / Highly Automated Vehicles (AVs) have the capability to revolutionize the transportation system. These systems have the possibility to make roads safer as AVs do not have many of the limitations that human drivers do, many of which are common causes of vehicle crashes (e.g., distraction or fatigue). AVs will be expected to drive alongside human drivers, and so these drivers are likely to continue to be at fault in causing crashes. As part of ensuring safety, AVs will reasonably be expected to try and avoid or help reduce the severity of these crashes. AVs operate using three main systems: the perception system which consists of sensors that see the objects around the AV, the decision and path planning system, which makes decision on what the AV will do, and the vehicle motion control system. Due to the nature of the real-world, these systems may not work exactly as intended which may affect the ability of the AV to react to possible crash scenarios. Because of this, the goal of this study was to investigate the effects that perception system performance (i.e., target object state errors) has on the ability of an AV to react to crash scenarios similar to those created by human drivers. This study first defined crash scenarios using real-world crash data. A real-world perception system was then tested in these scenarios to determine object detection performance. Based on this performance, effects on safety were assessed through vehicle crash simulations. Results from this analysis showed that safety can vary based on both perception system performance and crash scenario. This highlights that it will be important to address system performance in order to achieve high levels of driving safety.