IP-Level Satellite Link Emulation with KauNet

Distributed applications and transport protocols communicating over a satellite link may react very strongly to conditions specific to that kind of link. Providing a evaluation framework to allow tests of real implementations of such software in that context is quite a challenging task. In this paper we demonstrate how the use of the general-purpose KauNet IP-level emulator combined with satellite-specific packet loss patterns can help by reproducing losses and delays experienced on a satellite link with a simple Ethernet LAN setup. Such a platform is an essential tool for developers performing continuous testing as they provide new features for e.g. video codecs or transport-level software like DCCP and its congestion control components

Validation of a Driving Simulator for Road Tunnel Behavioural Studies

Introduction. According to European regulations, road tunnel safety is strategic in the management of national and international road corridors. Although the accident rate is lower in tunnels than on open roads, the severity of crashes in tunnels is higher due to the presence of hard lateral obstacles and limited space in case of lane departure. Driving simulation studies can support design decisions to assess the impact of any safety improvement albeit driving simulators must be validated to understand how the experimental results relate to real driving conditions. Method. This study deals with the behavioural validation of the fixed base driving simulator of the RSDS Lab for safety studies for tunnels. Field speed and lateral position data for vehicles were collected by image analysis of video sequences collected from the CCTV cameras in five sections of the Fréjus tunnel (Italy-France). The tunnel was faithfully modelled in the virtual scenario, and the same data were collected by extracting records at the same cameras’ stations. Thirty-five participants were involved in a between-subject experiment. Fifteen drivers with Italian B licenses drove a car, and twenty professional drivers with Italian C and/or D licenses drove a heavy truck. Results and Conclusions. Normality tests for data distributions and t-tests for the comparison between real and simulated data were conducted. The simulator achieved the relative validation for truck speeds (with values observed in the simulation always lower than those observed in real driving), and absolute validation with regard to truck lateral position. Opposite outcomes were obtained for cars, with absolute validity for speed and relative validity for lateral position. The relative-absolute validation of the driving simulator enables us to establish how experimental outcomes can be generalized to understand the impact of any safety countermeasure

Ray Launching Modeling in Curved Tunnels with Rectangular or Non Rectangular Section

International audienceSeveral methods to model radio wave propagation in tunnels have been published in the literature and will be presented in this chapter with their advantages and drawbacks. Among them, only few works are dedicated to non rectangular cross section and curved tunnels. Hence, we focus on a new method recently developed. The structure of the chapter is as follows. Section 2 presents the context of the works and why deployments of wireless telecommunication systems are needed for transport applications. Existing techniques to model radio wave propagation in tunnel are presented in section 3 with their respective advantages and drawbacks. The fourth and fifth sections are respectively devoted to the design and the evaluation of a propagation prediction model for curved tunnel with a rectangular or a circular cross section. Finally, section 6 concludes and presents some perspectives to these works

Development and Application of Fire Video Image Detection Technology in China’s Road Tunnels

A large number of highway tunnels, urban road tunnels and underwater tunnels have been constructed throughout China over the last two decades. With the rapid increase in vehicle traffic, the number of fire incidents in road tunnels have also substantially increased. This paper aims to review the development and application of fire video image detection (VID) technology and their impact on fire safety in China’s road tunnels. The challenges of fire safety in China’s road tunnels are analyzed. The capabilities and limitations of fire detection technologies currently used in China’s road tunnels are discussed. The research and development of fire VID technology in road tunnels, including various detection algorithms, evolution of VID systems and evaluation of their performances in various tunnel tests are reviewed. Some cases involving VID applications in China’s road tunnels are reported. The studies show that the fire VID systems have unique features in providing fire protection and their detection capability and reliability have been enhanced over the decades with the advance in detection algorithms, hardware and integration with other tunnel systems. They have become an important safety system in China’s road tunnels

Aeronautical Engineering: A continuing bibliography with indexes, supplement 99

This bibliography lists 292 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1978

An Outdoor Stereo Camera System for the Generation of Real-World Benchmark Datasets with Ground Truth

In this report we describe a high-performance stereo camera system to capture image sequences with high temporal and spatial resolution for the evaluation of various image processing tasks. The system was primarily designed for complex outdoor and traffic scenes which frequently occur in the automotive industry, but is also suited for other applications. For this task the system is equipped with a very accurate inertial measurement unit and global positioning system, which provides exact camera movement and position data. The system is already in active use and has produced several terabyte of challenging image sequences which are available for download

An information assistant system for the prevention of tunnel vision in crisis management

In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions

Explainable and Advisable Learning for Self-driving Vehicles

Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers, etc., can understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. Our work has focused on the challenge of generating introspective explanations of deep models for self-driving vehicles. In Chapter 3, we begin by exploring the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. In Chapter 4, we add an attention-based video-to-text model to produce textual explanations of model actions, e.g. "the car slows down because the road is wet". The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. These explainable systems represent an externalization of tacit knowledge. The network's opaque reasoning is simplified to a situation-specific dependence on a visible object in the image. This makes them brittle and potentially unsafe in situations that do not match training data. In Chapter 5, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present the first step toward advice-giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Further, in Chapter 6, we propose a new approach that learns vehicle control with the help of long-term (global) human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. "I see a pedestrian crossing, so I stop"), and predict the controls, accordingly