Traffic Light Recognition for Real Scenes Based on Image Processing and Deep Learning

Traffic light recognition in urban environments is crucial for vehicle control. Many studies have been devoted to recognizing traffic lights. However, existing recognition methods still face many challenges in terms of accuracy, runtime and size. This paper presents a novel robust traffic light recognition approach that takes into account these three aspects based on image processing and deep learning. The proposed approach adopts a two-stage architecture, first performing detection and then classification. In the detection, the perspective relationship and the fractal dimension are both considered to dramatically reduce the number of invalid candidate boxes, i.e. region proposals. In the classification, the candidate boxes are classified by SqueezeNet. Finally, the recognized traffic light boxes are reshaped by postprocessing. Compared with several reference models, this approach is significantly competitive in terms of accuracy and runtime. We show that our approach is lightweight, easy to implement, and applicable to smart terminals, mobile devices or embedded devices in practice

Annual performance indicators of enforced driver behaviours in South Australia, 2007

This report was produced to quantify performance indicators for selected enforced driver behaviours (drink driving, drug driving, speeding and restraint use) in South Australia for the calendar year 2007. The level of random breath testing (RBT) in South Australia in 2007 decreased slightly but remained at a relatively high level. The proportion of tests conducted using mobile RBT continued to increase. The detection rate based on evidentiary testing increased in 2007 to the highest level on record, while the detection rate for screening tests decreased. Detection rates in South Australia were comparable with those in other states. Just over 12,000 drug tests were conducted during 2007, the first full year of random drug testing. Relative to other Australian jurisdictions supplying comparative data, South Australia had the highest testing rate per head of population. Around 24 drivers per 1,000 tested were confirmed positive for at least one of the three prescribed drugs with methylamphetamine the most commonly detected drug. Of the fatally injured drivers who were drug tested in 2007, 25 per cent tested positive for illicit drugs. There was a slight decrease in the number of hours spent on speed detection in 2007. Nevertheless, the total number of speed detections increased, with increases observed for speed camera and red light/speed cameras, the latter most likely due to the expansion of the program. The detection rate (per hour of enforcement and per 1,000 vehicles passing speed cameras) increased by around 30 per cent. Data from systematic speed surveys, introduced in 2007, indicated that travelling speeds on South Australian roads were increasing. The number of restraint offences in 2007 decreased by 14 per cent. Males were charged with more restraint offences and were more likely to be unrestrained in fatal and serious injury crashes than females, indicating that males remain an important target for restraint enforcement. The 2007 publicity campaign focused on the consequences of not using restraints rather than increasing the perceived risk of detection.LN Wundersitz, K Hiranandani, MRJ Baldoc

Defending against Sybil Devices in Crowdsourced Mapping Services

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio

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

Map++: A Crowd-sensing System for Automatic Map Semantics Identification

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2014