Traffic signs recognition for detailed digital maps development and driver assistance systems

Digital maps are considered as an additional sensor in many of the new ADAS, but these systems usually require a higher level of accuracy and detail of the maps. Among the important information that the maps should contain are the road geometry and traffic signs. In the first case, it is interesting to use accurate and fast methods for measurement. In the paper, a method based on a datalog vehicle is used. Satellite positioning and inertial measurements systems data are combined and dynamic behavior of the vehicle body is corrected measuring the movements of the suspension system. On the other hand, the information provided by traffic signs and route-guidance signs is extremely important for safe and successful driving. An automatic system that is capable of extracting and identifying these signs automatically would help human drivers enormously; navigation would be easier, allowing them to concentrate on driving the vehicle. A Computer Vision System is used to recognize and classify the different families of traffic signs combining it with GPS information to develop detailed and accurate digital maps. This sign recognition can also be used for real time warnings to the driver. Some results of test carried out in real situations are shown

Regenerative and anti-lock braking system in electric vehicles

Many accidents are caused when a vehicle is braked hard, causing the wheels to lock up. At such times, the driver has no control over the steering of the vehicle and as a result cannot change the direction of the car. Anti-Lock Braking System prevents wheels from being locked up during braking by using a non continuous form of braking known as Pulse Width Modulation (PWM) braking. This gives the driver the control of the vehicle at all times and even while braking. Because of such type of braking, the wheels can better grip the road surface and the stopping distances also reduce significantly especially on tricky road surfaces like icy or wet roads. The kinetic energy of the wheel is generally lost during braking in the form of heat due to friction between the brake pads. This energy can be recovered using a technique called as Regenerative Braking. In this technique, the excess energy is stored temporarily in capacitor banks before it gets converted to heat energy and is wasted. This system prolongs the battery life by recharging the battery using the stored energy. Hence the mileage of the electric vehicle also increases as it can travel more distance in a single battery charge. These two methods together help make an electric vehicle energy efficient as well as safer and easier to use thus preventing and reducing the number of accidents

Reconhecimento automático de sinalização vertical de trânsito a partir de dados vídeo de um sistema de mapeamento móvel

Tese de mestrado em Engenharia Geográfica, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2013Este trabalho consiste no estudo e desenvolvimento de um método de Reconhecimento Automático de Sinalização Vertical de Trânsito (RASVT), nomeadamente da sinalização de perigo e alguma da sinalização de regulamentação (cedência de passagem, proibição e obrigação), toda de cor vermelha ou azul. O ponto de partida para o seu desenvolvimento são os dados vídeo de um Sistema de Mapeamento Móvel, obtidos em condições reais (não controladas). A metodologia desenvolvida de reconhecimento de sinalização vertical de trânsito funciona em modo pós-aquisição, ou seja, após aquisição e processamento dos dados vídeo em bruto (raw) e dos dados de posicionamento (GPS e IMU). O método desenvolvido é constituído por três fases. A fase de detecção, que consiste em detectar Regiões de Interesse da imagem (RdI) que possam conter sinalização. Esta detecção é feita utilizando um processo de segmentação por cor (vermelha e azul) e utilizando cinco critérios de selecção: dimensões da RdI, rácio altura-comprimento da RdI, proximidade da RdI aos limites da imagem, posicionamento relativo entre o centróide da RdI e centróide da área segmentada e rácio de preenchimento da RdI. A fase de classificação, que tem por base as RdI obtidas na fase anterior e consiste no reconhecimento da forma geométrica de cada região, bem como na agregação, por classes, de cada uma dessas RdI analisadas. As classes utilizadas baseiam-se na cor e forma geométrica. A fase de reconhecimento, consiste na identificação da sinalização que tenha sido detectada e classificada e baseia-se na correspondência dos pictogramas presentes nos sinais. Através de um processo de segmentação por cor é extraído o pictograma para de seguida ser feito o seu reconhecimento, através da correspondência entre o pictograma extraído e pictogramas template. Esta correspondência é realizada utilizando a correlação simples. A fase de detecção apresentou uma taxa de sucesso de 32 % (que excluindo os resultados falsos positivos, apresenta uma taxa de sucesso de 89 %), a fase de classificação teve uma taxa de sucesso de 93 % e a fase de reconhecimento teve uma taxa de sucesso de 91 %. A taxa de sucesso global obtido pelo método RASVT implementado é de 81 %, ou seja, da sinalização presente na amostra analisada, 81 % foi correctamente detectada, classificada e reconhecida.This work aims to develop a method for Automatic Traffic Sign Recognition, namely, danger signs and some regulation signs (giving way, prohibition and obligation signs, all of them red or blue). The starting point for developing the method was a Mobile Mapping System’s video data, obtained in real conditions (uncontrolled conditions). The Automatic Traffic Sign Recognition method performs data post-processing (not real time processing) and comprises three stages. The detection stage consists in detecting the image Regions Of Interest (ROI) that may contain signs. This detection is performed using a color segmentation process (red and blue) and using five selection criteria (ROI dimensions, length-height ratio of the ROI, ROI distance to the limits of the image, the relative positioning between ROI’s centroid and a targeted area’s centroid and finally the filling ratio of ROI). The step of classification is based on the ROI obtained in the previous stage and consists in recognizing each region’s geometric shape, as well as the aggregation per classes of each one of these ROI. The classes used are based on color and geometric shapes. The recognition stage consists in the identification of the traffic signs that has been detected and classified, and is based on the correspondence of the pictograms present in the signals. Through a process of color segmentation the pictogram is extracted from the ROI and then its recognition is done by matching with template pictograms. This matching is performed using simple correlation. The detection stage showed a success rate of 32 % (if we exclude false positive results, the success rate increases to 89 %), the classification stage had a success rate of 93 % and the final recognition stage had a success rate of 91 %. The overall success rate obtained by the implemented method is 81 %, i.e., from the totality of traffic signs present in the sample, 81 % were correctly detected, classified and recognized

Conception d’un simulateur de conduite pour véhicule Spyder

Le Spyder conçu par BRP est un véhicule complexe, original et peu connu du grand public à travers le monde. Par conséquent, on souhaite développer un outil facile d’accès pour la découverte, la formation au pilotage et l’aide à la conception du roadster. Le sujet de ce projet de maîtrise est le développement du modèle dynamique du véhicule et l’intégration à une plateforme de simulation existante. Pour y parvenir, on élabore un modèle réaliste du véhicule sous deux versions, sport et tourisme. Après avoir recherché les différents paramètres et caractéristiques du véhicule, on se concentre d’abord sur un modèle simple puis sur un modèle plus complexe comportant différents modules, comme la motorisation et les aides à la conduite. En vue de valider le modèle, on utilisera les résultats de tests et des mesures expérimentales. Après validation, le modèle doit être intégré à la nouvelle plateforme de simulation. Le logiciel, développé en langage C++, est élaboré à partir de la plateforme de base. Des modèles 3D détaillés du Spyder offrent un rendu graphique réaliste pour une meilleure immersion. Le modèle est capable de répondre en temps réel et de manière réaliste et précise sous le contrôle de l’utilisateur. On a donc un outil polyvalent à objectifs multiples : faire connaître le véhicule, aider l’ingénieur dans l’étude du véhicule et former les futurs pilotes de manière plus efficace et moins coûteuse. L’outil de simulation peut être également un moyen d’évaluer facilement des paramètres dont l’appréciation est subjective comme la signature sonore du véhicule

Energy Conservation and Security Enhancement in Wireless End-to-end Secure Connections

Wireless channels are vulnerable to interception. In some applications an end-to-end secure data transfer is required. However the use of cryptographic functions in communication over a wireless channel increases sensitivity to channel errors. As a result, the connection characteristics in terms of delay, throughput, and transmission energy worsen. Transmission energy is a key issue in some secure end-to-end wireless applications especially if they are running on mobile handheld devices with a limited source of energy such as batteries. That is why in most secure end-to-end wireless connections, the connection is dropped in poor channel conditions. In this thesis, models are proposed by which the performance is improved and transmission energy is lowered. A combination of a cross-layer controller, K Best Likelihood (K-BL) channel decoder, and a keyed error detection algorithm in the novel model supports the authorized receivers by a higher throughput, lower delay mean, and less transmission energy in a certain range of the Signal to Noise Ratio (SNR). This is done at the expense of additional computation at the receiving end. Ttradeoffs are examined and the simulation results of the new model are compared with those of conventional wireless communication systems. Another model is devised to mitigate the energy consumption of the Turbo Code channel decoder. The overall decoding energy consumption for each packet can be lowered by reducing the average number of iterations in the Turbo Code channel decoder. The proposed models achieve better energy consumption by reducing the number of iterations in a channel decoder that uses the Turbo decoder and by reducing the number of retransmissions in a trellis channel decoder. Furthermore, the security enhancement of the novel models is assessed in terms of the extent to which the enhancement is fully achieved