A Vision Based Lane Marking Detection, Tracking and Vehicle Detection on Highways

Changing street conditions is an important issue in the applications in mechanized route of vehicles essentially because of vast change in appearance in lane markings on by variables such substantial movement and changing daylight conditions of the specific time of day. A path identification framework is an imperative segment of numerous computerized vehicle frameworks. In this paper, we address these issues through lane identification and vehicle recognition calculation to manage testing situations, for example, a lane end and flow, old lane markings, and path changes. Left and right lane limits will be distinguished independently to adequately handle blending and part paths utilizing a strong calculation. Vehicle discovery is another issue in computerized route of vehicles. Different vehicle discovery approaches have been actualized yet it is hard to locate a quick and trusty calculation for applications, for example, for vehicle crashing (hitting) cautioning or path evolving system .Vision-based vehicle recognition can likewise enhance the crash cautioning execution when it is consolidated with a lane marking identification calculation. In crash cautioning applications, it is vital to know whether the obstruction is in the same path with the sense of self vehicle or not

Integrated trajectory planning and control for obstacle avoidance manoeuvre using nonlinear vehicle model-predictive algorithm

In the current literature, model-predictive (MP) algorithm is widely applied in autonomous vehicle trajectory planning and control but most of the current studies only apply the linear tyre model, which cannot accurately present the tyre non-linear characteristic. Furthermore, most of these studies separately consider the trajectory planning and trajectory control of the autonomous vehicle and few of them have integrated the trajectory planning and trajectory control together. To fill in above research gaps, this study proposes the integrated trajectory planning and trajectory control method using a non-linear vehicle MP algorithm. To fully utilise the advantages of four-wheel-independent-steering and four-wheel-independent-driving vehicle, the MP algorithm is proposed based on four-wheel dynamics model and non-linear Dugoff tyre model. This study also proposes the mathematical modelling of the static obstacle and dynamic obstacle for the obstacle avoidance manoeuvre of the autonomous vehicle. Finally, simulation results have been presented to show the effectiveness of the proposed control method

Online sensor information and redundancy resolution based obstacle avoidance for high DOF mobile manipulator teleoperation

High degrees of freedom (DOF) mobile manipulators provide more flexibility than conventional manipulators. They also provide manipulation operations with a mobility capacity and have potential in many applications. However, due to high redundancy, planning and control become more complicated and difficult, especially when obstacles occur. Most existing obstacle avoidance methods are based on off-line algorithms and most of them mainly focus on planning a new collision-free path, which is not appropriate for some applications, such as teleoperation and uses many system resources as well. Therefore, this paper presents an online planning and control method for obstacle avoidance in mobile manipulators using online sensor information and redundancy resolution. An obstacle contour reconstruction approach employing a mobile manipulator equipped with an active laser scanner system is also introduced in this paper. This method is implemented using a mobile manipulator with a seven-DOF manipulator and a four-wheel drive mobile base. The experimental results demonstrate the effectiveness of this method. © 2013 Zhang et al.; licensee InTech.Link_to_subscribed_fulltex

Studio di tecniche di visione artificiale in tempo reale e loro implementazione per applicazioni industriali

L’attvità svolta durante il corso di Dottorato ha avuto l'obbiettivo di capire dove e come i sistemi di visione artificiale possono integrare o sostituire precedenti tecnologie applicate nel campo dell'automazione industriale migliorandone le prestazioni. Vengono proposti sistemi innovativi di elaborazione immagine che sostituiscono quelli attuali implementati con altre tecnologie e viene illustrato come il nuovo dispositivo si interfaccia con il sistema generale proponendo quindi tecniche di interfacciamento in tempo reale

Eit litteraturstudie på objektdeteksjon og attkjenning av køyretøy i ei køyrebane

Master's thesis in Cybernetics and signal processingEi mykje omtalt problemstilling er overgangen frå bilar styrt av menneske til autonome bilar som må lese trafikkbilde fortløpande. Utviklinga er komen så langt at all form for automasjon og målesystem som trengs for å få nødvendig informasjon allereie er på plass. Spørsmålet framover vil vere kvar det kan kuttast ned på kostnadar men likevel ha eit robust system. Hovudmålet for denne oppgåva er å gje innsyn i utviklinga innanfor køyretøydeteksjon fram til dagens «state-of-art» med eit hovudfokus på kamerasyn. Teknologien som dei kommersielle bilfabrikantane nyttar er proprietær, og sidan det ikkje er mogleg å få innsyn i dette er det antatt at dei nyttar nokon av dei presenterte metodane. Ei analyse er gjort på bakgrunn av opne rapportar som presentera metodar for å detektere køyretøy i eit køyrefelt. Rapporten presentera ei oversikt over sensorar og metodar som blir brukt for å skilje mellom køyretøy og ulike objekt i eit trafikkbilde. Det er valt å sortere arbeidet inn i monosyn, stereosyn og ein fusjon av sensorar slik som kamera, radar og lidar. Fokuset i dette feltet er hurtig skiftande, og det har gått ifrå enkle metodar som søk etter køyretøy på bakgrunn av symmetri, til komplekse eigenskapar som blir definert av djupe nevrale nett og punktskyar frå aktive sensorar. Det mest lovande arbeidet for å nytte i eit sjølvstyrt køyretøy basera seg på ein fusjon mellom aktive og passive sensorar som kontinuerleg har eit overblikk over miljøet rundt køyre-tøyet

Model-driven and Data-driven Approaches for some Object Recognition Problems

Recognizing objects from images and videos has been a long standing problem in computer vision. The recent surge in the prevalence of visual cameras has given rise to two main challenges where, (i) it is important to understand different sources of object variations in more unconstrained scenarios, and (ii) rather than describing an object in isolation, efficient learning methods for modeling object-scene `contextual' relations are required to resolve visual ambiguities. This dissertation addresses some aspects of these challenges, and consists of two parts. First part of the work focuses on obtaining object descriptors that are largely preserved across certain sources of variations, by utilizing models for image formation and local image features. Given a single instance of an object, we investigate the following three problems. (i) Representing a 2D projection of a 3D non-planar shape invariant to articulations, when there are no self-occlusions. We propose an articulation invariant distance that is preserved across piece-wise affine transformations of a non-rigid object `parts', under a weak perspective imaging model, and then obtain a shape context-like descriptor to perform recognition; (ii) Understanding the space of `arbitrary' blurred images of an object, by representing an unknown blur kernel of a known maximum size using a complete set of orthonormal basis functions spanning that space, and showing that subspaces resulting from convolving a clean object and its blurred versions with these basis functions are equal under some assumptions. We then view the invariant subspaces as points on a Grassmann manifold, and use statistical tools that account for the underlying non-Euclidean nature of the space of these invariants to perform recognition across blur; (iii) Analyzing the robustness of local feature descriptors to different illumination conditions. We perform an empirical study of these descriptors for the problem of face recognition under lighting change, and show that the direction of image gradient largely preserves object properties across varying lighting conditions. The second part of the dissertation utilizes information conveyed by large quantity of data to learn contextual information shared by an object (or an entity) with its surroundings. (i) We first consider a supervised two-class problem of detecting lane markings from road video sequences, where we learn relevant feature-level contextual information through a machine learning algorithm based on boosting. We then focus on unsupervised object classification scenarios where, (ii) we perform clustering using maximum margin principles, by deriving some basic properties on the affinity of `a pair of points' belonging to the same cluster using the information conveyed by `all' points in the system, and (iii) then consider correspondence-free adaptation of statistical classifiers across domain shifting transformations, by generating meaningful `intermediate domains' that incrementally convey potential information about the domain change

Back Lane Marking Registry: uma abordagem de localização e seguimento de caminho por veículos autônomos em via sinalizada

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