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Singapore startup, nuTonomy has partnered with Grab Taxi to begin trials.
That will usher in the world’s first autonomous taxis. But a minor accident with a lorry put a bumper in its plans.
How Safe Are Self-driving cars? Telsa's computer vision-based vehicle detection system combined with GPS determines the car’s position on the road, warning drivers of an impending collision when objects stray into their path. But Joshua Brown, 40, of Canton, Ohio, was killed driving a Tesla Model S with the 'self-drive' feature switched on when a trailer turned left into his path. The “technical failure” of the automatic braking system happened because the collision-avoidance system kicks in, only when radar and computer vision systems agree an obstacle lies ahead.
The Nevada Center for Advanced Mobility is building smart road infrastructure that allows for the vehicle-to-infrastructure and vehicle-to-vehicle communications. The partnerships between the state, private and academic entities, helps Nevada track vehicles and what they see while the state concentrates on the infrastructure. The information gathered from this pilot program render cars into rolling 'data centres' eager to utilise and distribute data.
For Tech Giants like Apple - who outlined plans to develop self-driving technology. Cook, however, was reluctant to reveal whether Apple will eventually produce its own self-driving car. Choosing instead to invest $1 billion last year in Didi Chuxing, the biggest Chinese ride-hailing service.
For Entreprenuers like Elon Musk - who revealed plans to bore a series of tunnels under major cities. Work on its first tunnel beneath Los Angeles, which will run from Los Angeles to Sherman Oaks has begun. Inside these tunnels, cars will sit on pods that travel 200 kilometres per hour, going from Westwood to Los Angeles in 5-6 minutes.
For Cities like Delft - who built a 30-meter long test facility which can support tests on a full-scale Hyperloop pod at low speeds. Hyperloops are a driverless high-speed transportation system where pressurised passenger passenger cabins, travel at speeds of 600+ mph driven by linear induction motors and air compressors. Due to its large urban populations, the Netherlands is the most logical place to install the Hyperloop.
Abstract: The world population is growing at a rapid pace. Towns and cities are accommodating half of the world's population thereby creating tremendous pressure on every aspect of urban living. Cities are known to have large concentration of resources and facilities. Such environments attract people from rural areas. However, unprecedented attraction has now become an overwhelming issue for city governance and politics. The enormous pressure towards efficient city management has triggered various Smart City initiatives by both government and private sector businesses to invest in ICT to find sustainable solutions to the growing issues. The Internet of Things (IoT) has also gained significant attention over the past decade. IoT envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities. Today infrastructure, platforms, and software applications are offered as services using cloud technologies. In this paper, we explore the concept of sensing as a service and how it fits with the Internet of Things. Our objective is to investigate the concept of sensing as a service model in technological, economical, and social perspectives and identify the major open challenges and issues.
Pub.: 30 Jul '13, Pinned: 17 Jun '17
Abstract: Smart city represents one of the most promising, prominent and challenging Internet of Things (IoT) applications . In the last few years, indeed, the smart city concept has played an important role in academic and industry fields, with the development and deployment of various middleware platforms and IoT‐based infrastructures. However, this expansion has followed distinct approaches creating, therefore, a fragmented scenario, in which different IoT ecosystems are not able to communicate between them. To fill this gap, there is a need to re‐visit the smart city IoT semantic and to offer a global common approach. To this purpose, this paper browses the semantic annotation of the sensors in Cloud, and innovative services can be implemented and considered by bridging Cloud of Things (CoT) and IoT. Things like semantic will be considered to perform the aggregation of heterogeneous resources by defining the CoT paradigm. We survey the smart city vision, providing information on the main requirements and highlighting the benefits of integrating different IoT ecosystems within Cloud under this new CoT vision. This paper also discusses relevant challenges in this research area. Copyright © 2015 John Wiley & Sons, Ltd.
Pub.: 01 Feb '15, Pinned: 17 Jun '17
Abstract: As of 2014, 54% of the earth's population resides in urban areas, and it is steadily increasing, expecting to reach 66% by 2050. Urban areas range from small cities with tens of thousands of people to megacities with greater than 10 million people. Roughly 12% of the global population today lives in 28 megacities, and at least 40 are projected by 2030. At these scales, the urban infrastructure such as roads, buildings, and utility networks will cover areas as large as New England. This steady urbanization and the resulting expansion of infrastructure, combined with renewal of aging urban infrastructure, represent tens of trillion of dollars in new urban infrastructure investment over the coming decades. These investments must balance factors including impact on clean air and water, energy and maintenance costs, and the productivity and health of city dwellers. Moreover, cost-effective management and sustainability of these growing urban areas will be one of the most critical challenges to our society, motivating the concept of science- and data-driven urban design, retrofit, and operation-that is, "Smart Cities".
Pub.: 04 May '17, Pinned: 15 Jun '17
Abstract: The concept of Intercultural Education is analyzed throughout the planet. This definition should have given a new twist in recent years, as society and, in particular, cities have entered or intend to do so in the idea of Smart City, which implies the adaptation and preparation of the population and education that will take place towards this situation. With this idea, we started an investigation to examine whether, the cities that erect the seal of Smart City, take actions to integrate the entire population and improve social inclusion. After analysing documents from the centre and the implementation of Intercultural Education in different schools, through observation and interviews to teachers, at different stages and cities, defined as Smart City, we got that, no school undertakes any action respect the integration and social inclusion of the population in their classrooms. Leading us to conclude that the label of Smart city is adding new social disadvantages, leading us to, among our recommendations, that the concept of Intercultural Education should be renewed, including the idea of Intelligent Citizens for Smart Cities.
Pub.: 08 Mar '17, Pinned: 15 Jun '17
Abstract: With the potential to save nearly 30 000 lives per year in the United States, autonomous vehicles portend the most significant advance in auto safety history by shifting the focus from minimization of postcrash injury to collision prevention. I have delineated the important public health implications of autonomous vehicles and provided a brief analysis of a critically important ethical issue inherent in autonomous vehicle design. The broad expertise, ethical principles, and values of public health should be brought to bear on a wide range of issues pertaining to autonomous vehicles. (Am J Public Health. Published online ahead of print February 16, 2017: e1-e6. doi:10.2105/AJPH.2016.303628).
Pub.: 17 Feb '17, Pinned: 15 Jun '17
Abstract: Autonomous vehicles are becoming an essential tool in a wide range of environmental applications that include ambient data acquisition, remote sensing, and mapping of the spatial extent of pollutant spills. Among these applications, pollution source localization has drawn increasing interest due to its scientific and commercial interest and the emergence of a new breed of robotic vehicles capable of operating in harsh environments without human supervision. The aim is to find the location of a region that is the source of a given substance of interest (e.g. a chemical pollutant at sea or a gas leakage in air) using a group of cooperative autonomous vehicles. Motivated by fast paced advances in this challenging area, this paper surveys recent advances in searching techniques that are at the core of environmental monitoring strategies using autonomous vehicles.
Pub.: 04 Mar '17, Pinned: 15 Jun '17
Abstract: The USA has the worst motor vehicle safety problem among high-income countries and is pressing forward with the development of autonomous automobiles to address it. Government guidance and regulation, still inadequate, will be critical to the safety of the public. The analysis of this public health problem in the USA reveals the key factors that will determine the benefits and risks of autonomous vehicles around the world.
Pub.: 25 Jan '17, Pinned: 15 Jun '17
Abstract: Publication date: Available online 15 September 2016 Source:Regional Science and Urban Economics Author(s): Roman Zakharenko The effects of autonomous vehicles (AVs) on urban forms are modeled, calibrated, and analyzed. Vehicles are used for commute between peripheral home and central work, and require land for parking. An advantage of AVs is that they can optimize the location of day parking, relieving downtown land for other uses. They also reduce the per-kilometer cost of commute. Increased AV availability increases worker welfare, traffic, travel distances, and the city size. Land rents increase in the center but decrease in the periphery. Possible locations of AV daytime parking are analyzed. The effects of AV introduction on traffic and on mass transit coverage are discussed.
Pub.: 17 Sep '16, Pinned: 15 Jun '17
Abstract: Suppose that a driverless car is headed toward five pedestrians. It can stay on course and kill them or swerve into a concrete wall, killing its passenger. On page 1573 of this issue, Bonnefon et al. (1) explore this social dilemma in a series of clever survey experiments. They show that people generally approve of cars programmed to minimize the total amount of harm, even at the expense of their passengers, but are not enthusiastic about riding in such “utilitarian” cars—that is, autonomous vehicles that are, in certain emergency situations, programmed to sacrifice their passengers for the greater good. Such dilemmas may arise infrequently, but once millions of autonomous vehicles are on the road, the improbable becomes probable, perhaps even inevitable. And even if such cases never arise, autonomous vehicles must be programmed to handle them. How should they be programmed? And who should decide? Author: Joshua D. Greene
Pub.: 24 Jun '16, Pinned: 15 Jun '17
Abstract: This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle (AV) technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure (connected vehicles). A major knowledge gap exists in AV technology with respect to routing behaviors. Connected-vehicle technology provides a great opportunity to implement an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization. This study contributes to the literature on two fronts: (i) it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and (ii) it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.
Pub.: 29 Aug '16, Pinned: 15 Jun '17
Abstract: The detection of small road hazards, such as lost cargo, is a vital capability for self-driving cars. We tackle this challenging and rarely addressed problem with a vision system that leverages appearance, contextual as well as geometric cues. To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework. Here a variant of a fully convolutional network is used to predict a pixel-wise semantic labeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii) background. The geometric cues are exploited using a state-of-the-art detection approach that predicts obstacles from stereo input images via model-based statistical hypothesis tests. We present a principled Bayesian framework to fuse the semantic and stereo-based detection results. The mid-level Stixel representation is used to describe obstacles in a flexible, compact and robust manner. We evaluate our new obstacle detection system on the Lost and Found dataset, which includes very challenging scenes with obstacles of only 5 cm height. Overall, we report a major improvement over the state-of-the-art, with relative performance gains of up to 50%. In particular, we achieve a detection rate of over 90% for distances of up to 50 m. Our system operates at 22 Hz on our self-driving platform.
Pub.: 20 Dec '16, Pinned: 15 Jun '17
Abstract: This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).
Pub.: 28 Dec '16, Pinned: 15 Jun '17
Abstract: To lay the basis of studying autonomous driving comfort using driving simulators, we assessed the behavioral validity of two moving-base simulator configurations by contrasting them with a test-track setting.With increasing level of automation, driving comfort becomes increasingly important. Simulators provide a safe environment to study perceived comfort in autonomous driving. To date, however, no studies were conducted in relation to comfort in autonomous driving to determine the extent to which results from simulator studies can be transferred to on-road driving conditions.Participants ( N = 72) experienced six differently parameterized lane-change and deceleration maneuvers and subsequently rated the comfort of each scenario. One group of participants experienced the maneuvers on a test-track setting, whereas two other groups experienced them in one of two moving-base simulator configurations.We could demonstrate relative and absolute validity for one of the two simulator configurations. Subsequent analyses revealed that the validity of the simulator highly depends on the parameterization of the motion system.Moving-base simulation can be a useful research tool to study driving comfort in autonomous vehicles. However, our results point at a preference for subunity scaling factors for both lateral and longitudinal motion cues, which might be explained by an underestimation of speed in virtual environments.In line with previous studies, we recommend lateral- and longitudinal-motion scaling factors of approximately 50% to 60% in order to obtain valid results for both active and passive driving tasks.
Pub.: 23 Dec '16, Pinned: 15 Jun '17
Abstract: This paper discusses opportunities to parallelize graph based path planning algorithms in a time varying environment. Parallel architectures have become commonplace, requiring algorithm to be parallelized for efficient execution. An additional focal point of this paper is the inclusion of inaccuracies in path planning as a result of forecast error variance, accuracy of calculation in the cost functions and a different observed vehicle speed in the real mission than planned. In this context, robust path planning algorithms will be described. These algorithms are equally applicable to land based, aerial, or underwater mobile autonomous systems. The results presented here provide the basis for a future Research project in which the parallelized algorithms will be evaluated on multi and many core systems such as the dual core ARM Panda board and the 48 core Single-chip Cloud Computer (SCC). Modern multi and many core processors support a wide range of performance vs. energy tradeoffs that can be exploited in energyconstrained environments such as battery operated autonomous underwater vehicles. For this evaluation, the boards will be deployed within the Slocum glider, a commercially available, buoyancy driven autonomous underwater vehicle (AUV).
Pub.: 26 Feb '17, Pinned: 15 Jun '17
Abstract: Abstract Recently, pedestrian detection systems have become an important technology in the development of the advanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) is currently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as one single feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOG decreases markedly. To solve this problem, we propose a detection system using a deformable part model (DPM) that divides the pedestrian data into two parts using a latent support vector machine (SVM)-based machine-learning technique. Experimental results show that our approach achieves better performance in a detection system than the existing method. In practice, there are many occlusions in the environment in front of the vehicle. For example, the surrounding transport facilities, such as a car or another obstacle, can occlude a pedestrian. These occlusions can increase the false detection rate and cause difficulties during the detection process. Our proposed method uses a different approach and can easily be applied in real-world scenarios, regardless of occlusions.AbstractRecently, pedestrian detection systems have become an important technology in the development of the advanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) is currently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as one single feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOG decreases markedly. To solve this problem, we propose a detection system using a deformable part model (DPM) that divides the pedestrian data into two parts using a latent support vector machine (SVM)-based machine-learning technique. Experimental results show that our approach achieves better performance in a detection system than the existing method. In practice, there are many occlusions in the environment in front of the vehicle. For example, the surrounding transport facilities, such as a car or another obstacle, can occlude a pedestrian. These occlusions can increase the false detection rate and cause difficulties during the detection process. Our proposed method uses a different approach and can easily be applied in real-world scenarios, regardless of occlusions.
Pub.: 25 Oct '16, Pinned: 15 Jun '17
Abstract: Abstract The possibility of using data from multiplex networks on vehicles in road tests, in the development of intelligent transportation systems, and in control systems for autonomous (self-driving) vehicles is considered.AbstractThe possibility of using data from multiplex networks on vehicles in road tests, in the development of intelligent transportation systems, and in control systems for autonomous (self-driving) vehicles is considered.
Pub.: 01 Oct '16, Pinned: 15 Jun '17
Abstract: Detecting small obstacles on the road ahead is a critical part of the driving task which has to be mastered by fully autonomous cars. In this paper, we present a method based on stereo vision to reliably detect such obstacles from a moving vehicle. The proposed algorithm performs statistical hypothesis tests in disparity space directly on stereo image data, assessing freespace and obstacle hypotheses on independent local patches. This detection approach does not depend on a global road model and handles both static and moving obstacles. For evaluation, we employ a novel lost-cargo image sequence dataset comprising more than two thousand frames with pixelwise annotations of obstacle and free-space and provide a thorough comparison to several stereo-based baseline methods. The dataset will be made available to the community to foster further research on this important topic. The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery. Small obstacles down to the height of 5 cm can successfully be detected at 20 m distance at low false positive rates.
Pub.: 15 Sep '16, Pinned: 15 Jun '17
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