VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Car make and model recognition system using rear-lamp features and convolutional neural networks

Recognizing cars based on their features is a difficult task. We propose a solution that uses a convolutional neural network (CNN) and image binarization method for car make and model classification. Unlike many previous works in this area, we use a feature extraction method combined with a binarization method. In the first stage of the pre-processing part we normalize and change the size of an image. The image is then used to recognize where the rear-lamps are placed on the image. We extract the region and use the image binarization method. The binarized image is used as input to the CNN network that finds the features of a specific car model. We have tested the combinations of three different neural network architectures and eight binarization methods. The convolutional neural network with parameters of the highest quality metrics value is used to find the characteristics of the rear lamps on the binary image. The convolutional network is tested with four different gradient algorithms. We have tested the method on two data sets which differ in the way the images were taken. Each data set consists of three subsets of the same car, but is scaled to different image dimensions. Compared to related works that are based on CNN, we use rear view images in different position and light exposure. The proposed method gives better results compared to most available methods. It is also less complex, and faster to train compared to other methods. The proposed approach achieves an average accuracy of 93,9% on the first data set and 84,5% on the second set

Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions

Avoiding high computational costs and calibration issues involved in stereo-vision-based algorithms, this paper proposes real-time monocular-vision-based techniques for simultaneous vehicle detection and inter-vehicle distance estimation, in which the performance and robustness of the system remain competitive, even for highly challenging benchmark datasets. This paper develops a collision warning system by detecting vehicles ahead and, by identifying safety distances to assist a distracted driver, prior to occurrence of an imminent crash. We introduce adaptive global Haar-like features for vehicle detection, tail-light segmentation, virtual symmetry detection, intervehicle distance estimation, as well as an efficient single-sensor multifeature fusion technique to enhance the accuracy and robustness of our algorithm. The proposed algorithm is able to detect vehicles ahead at both day or night and also for short- and long-range distances. Experimental results under various weather and lighting conditions (including sunny, rainy, foggy, or snowy) show that the proposed algorithm outperforms state-of-the-art algorithms

Car make and model recognition under limited lighting conditions at night

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyCar make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when licence plate numbers cannot be identified or fake number plates are used. CMMR can also be used when automatic identification of a certain model of a vehicle by camera is required. The majority of existing CMMR methods are designed to be used only in daytime when most car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. This work identifies car make and model at night by using available rear view features. A binary classifier ensemble is presented, designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and licence plates from the rear view are extracted and used in the recognition process. The majority vote of individual classifiers, support vector machine, decision tree, and k-nearest neighbours is applied to verify a target model in the classification process. The experiments on 100 car makes and models captured under limited lighting conditions at night against about 400 other car models show average high classification accuracy about 93%. The classification accuracy of the presented technique, 93%, is a bit lower than the daytime technique, as reported at 98 % tested on 21 CMMs (Zhang, 2013). However, with the limitation of car appearances at night, the classification accuracy of the car appearances gained from the technique used in this study is satisfied

Vehicle Keypoint Detection and Fine-Grained Classification using Deep Learning

Los sistemas de detección de puntos clave en vehículos y de clasificación por marca y modelo han visto como sus capacidades evolucionaban a un ritmo nunca antes visto, pasando de rendimientos pobres a resultados increíbles en cuestión de unos años. La irrupción de las redes neuronales convolucionales y la disponibilidad de datos y sistemas de procesamiento cada vez más potentes han permitido que, mediante el uso de modelos cada vez más complejos, estos y muchos otros problemas sean afrontados y resueltos con enfoques muy diversos. Esta tesis se centra en el problema de detección de puntos clave y clasificación a nivel de marca y modelo de vehículos con un enfoque basado en aprendizaje profundo. Tras el análisis de los conjuntos datos existentes para afrontar ambas tareas se ha optado por crear tres bases de datos específicas. La primera, orientada a la detección de puntos clave en vehículos, es una mejora y extensión del famoso conjunto de datos PASCAL3D+, reetiquetando parte del mismo y añadiendo nuevos keypoints e imágenes para aportar mayor variabilidad. La segunda, se trata de un conjunto de prueba de clasificación de vehículos por marca y modelo basado en The PREVENTION dataset, una base de datos de predicción de trayectoria de vehículos en entornos de circulación real. Por último, un conjunto de datos cruzados (Cross-dataset) compuesto por las marcas y modelos comunes de tres de las principales bases de datos de clasificación de vehículos, CompCars, VMMR-db y Frontal-103. El sistema de detección de puntos clave se basa en un método de detección de pose en humanos que mediante el uso de redes neuronales convolucionales y capas de-convolucionales genera, a partir de una imagen de entrada, un mapa de calor por cada punto clave. La red ha sido modificada para ajustarse al problema de detección de puntos clave en vehículos obteniendo resultados que mejoran el estado del arte sin hacer uso de complejas arquitecturas o metodologías. Adicionalmente se ha analizado la idoneidad de los puntos clave de PASCAL3D+, validando la propuesta de nuevos puntos clave como una mejor alternativa. El sistema de clasificación de vehículos por marca y modelo se basa en el uso de redes preentrenadas en el famoso conjunto de datos ImageNet y adaptadas al problema de clasificación de vehículos. Uno de los problemas detectados en el estado del arte es la saturación de los resultados en las bases de datos existentes que, por otra parte, se encuentran sesgadas, limitando la capacidad de generalización de los modelos entrenados con ellas. Se han usado múltiples técnicas de aprendizaje y ponderación de los datos para tratar de aliviar el impacto del sesgo de los conjuntos de datos. Para poder evaluar la capacidad de generalización en situaciones reales de los modelos entrenados, se ha hecho uso del conjunto de pruebas derivado del PREVENTION dataset. Adicionalmente, se ha hecho uso del Cross-dataset para evaluar la complejidad de las bases de datos existentes y las capacidades de generalización de los modelos entrenados con ellas. Se demuestra que, sin hacer uso de complejas arquitecturas, se pueden obtener resultados competitivos y la necesidad de un conjunto de datos que refleje de manera adecuada el mundo real para poder afrontar adecuadamente el problema de clasificación de vehículos.Vehicle keypoint detection and fine-grained classification systems have seen their capabilities evolve at an unprecedented rate, from poor performance to incredible results in a matter of a few years. The advent of convolutional neural networks and the availability of large amounts of data and progress in computational capabilities have allowed these and many other problems to be tackled and solved with very different approaches using increasingly complex models. This thesis focuses on the problems of keypoint detection and fine-grained classification of vehicles with a deep learning approach. After the analysis of the existing datasets to tackle both tasks, three new datasets have been built. The first one, oriented to the detection of keypoints in vehicles, is an improvement and extension of the famous PASCAL3D+ dataset, re-labelling part of it and adding new keypoints and images to provide more variability. The second is a vehicle make and model classification test set based on the PREVENTION dataset, a realworld driving scenario vehicle trajectory prediction dataset. Finally, a cross-dataset composed of common makes and models from three major vehicle classification databases, CompCars, VMMR-db and Frontal-103. The keypoint detection system is based on a human pose detection method that by using convolutional neural networks and deconvolutional layers generates, from an input image, a heat map for each keypoint. The network has been modified to fit the problem of keypoint detection in vehicles obtaining results that improve the state of the art without using complex architectures or methodologies. Additionally, the suitability of the PASCAL3D+ keypoints has been analysed, validating the proposal of new keypoints as a better alternative. The vehicle make and model classification system is based on the use of ImageNet pre-trained networks and fine-tuned for the vehicle classification problem. One of the problems detected in the state of the art is the saturation of the results in the existing datasets, which, moreover, are biased, limiting the generalisation capacity of the models trained with them. Multiple data learning and weighting techniques have been used to try to alleviate the impact of dataset bias. In order to assess the generalisation capabilities of the trained models in real situations, the PREVENTION test set has been used. Additionally, the cross-dataset has been used to evaluate the complexity of the existing datasets and the generalisation capabilities of the models trained with them. It is shown that competitive results can be achieved without the use of complex architectures and that a high quality dataset that adequately reflects the real world is needed in order to properly address the vehicle classification problem

3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection

Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and small, provide appearance information about the environment, and work in various weather conditions. They can be used for multiple purposes such as visual navigation and obstacle detection. We can use a surround multi-camera system to cover the full 360-degree field-of-view around the car. In this way, we avoid blind spots which can otherwise lead to accidents. To minimize the number of cameras needed for surround perception, we utilize fisheye cameras. Consequently, standard vision pipelines for 3D mapping, visual localization, obstacle detection, etc. need to be adapted to take full advantage of the availability of multiple cameras rather than treat each camera individually. In addition, processing of fisheye images has to be supported. In this paper, we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project. This project seeks to enable automated valet parking for self-driving cars. Our pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction