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

Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application

While the development of Vehicle-to-Vehicle (V2V) safety applications based on Dedicated Short-Range Communications (DSRC) has been extensively undergoing standardization for more than a decade, such applications are extremely missing for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between VRUs and vehicles was the main reason for this lack of attention. Recent developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this perspective. Leveraging the existing V2V platforms, we propose a new framework using a DSRC-enabled smartphone to extend safety benefits to VRUs. The interoperability of applications between vehicles and portable DSRC enabled devices is achieved through the SAE J2735 Personal Safety Message (PSM). However, considering the fact that VRU movement dynamics, response times, and crash scenarios are fundamentally different from vehicles, a specific framework should be designed for VRU safety applications to study their performance. In this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection based on the most common and injury-prone crash scenarios. The details of our VRU safety module, including target classification and collision detection algorithms, are explained next. Furthermore, we propose and evaluate a mitigating solution for congestion and power consumption issues in such systems. Finally, the whole system is implemented and analyzed for realistic crash scenarios

Indian Traffic Signboard Recognition and Driver Alert System Using Machine Learning

Sign board recognition and driver alert system which has a number of important application areas that include advance driver assistance systems, road surveying and autonomous vehicles. This system uses image processing technique to isolate relevant data which is captured from the real time streaming video. The proposed method is broadly divided in five part data collection, data processing, data classification, training and testing. System uses variety of image processing techniques to enhance the image quality and to remove non-informational pixel, and detecting edges. Feature extracter are used to find the features of image. Machine learning algorithm Support Vector Machine(SVM) is used to classify the images based on their features. If features of sign that are captured from the video matches with the trained traffic signs then it will generate the voice signal to alert the driver. In India there are different traffic sign board and they are classified into three categories: Regulatory sign, Cautionary sign, informational sign. These Indian signs have four different shapes and eight different colors. The proposed system is trained for ten different types of sign . In each category more than a thousand sample images are used to train the network

Car Traffic Sign Annunciator

Automatic detection and recognition of traffic signs is an essential part of automated driver assistance systems which contribute to the safety of the drivers, pedestrians and vehicles. This paper presents the advanced driver assistance system (ADAS) based on Raspberry pi for traffic sign detection, recognition and annunciation. Such a system presents a vital support for driver assistance in an intelligent automotive. The proposed algorithm is implemented in a real time embedded system using OpenCV library. Proposed method introduced a new method for detection and recognition of traffic signs. Firstly, Potential traffic signs regions are detected by colour segmentation method, then classified using HOG features and a linear SVM classifier to identify the traffic sign class. The proposed system shows good recognition rate under complex challenging lighting and weather conditions. Experimental results on the accuracy of the road sign detection are reported in this paper

Assessment of Driver\u27s Attention to Traffic Signs through Analysis of Gaze and Driving Sequences

A driver’s behavior is one of the most significant factors in Advance Driver Assistance Systems. One area that has received little study is just how observant drivers are in seeing and recognizing traffic signs. In this contribution, we present a system considering the location where a driver is looking (points of gaze) as a factor to determine that whether the driver has seen a sign. Our system detects and classifies traffic signs inside the driver’s attentional visual field to identify whether the driver has seen the traffic signs or not. Based on the results obtained from this stage which provides quantitative information, our system is able to determine how observant of traffic signs that drivers are. We take advantage of the combination of Maximally Stable Extremal Regions algorithm and Color information in addition to a binary linear Support Vector Machine classifier and Histogram of Oriented Gradients as features detector for detection. In classification stage, we use a multi class Support Vector Machine for classifier also Histogram of Oriented Gradients for features. In addition to the detection and recognition of traffic signs, our system is capable of determining if the sign is inside the attentional visual field of the drivers. It means the driver has kept his gaze on traffic signs and sees the sign, while if the sign is not inside this area, the driver did not look at the sign and sign has been missed

Advanced Driver-Assistance System with Traffic Sign Recognition for Safe and Efficient Driving

Advanced Driver-Assistance Systems (ADAS) coupled with traffic sign recognition could lead to safer driving environments. This study presents a sophisticated, yet robust and accurate traffic sign detection system using computer vision and ML, for ADAS. Unavailability of large local traffic sign datasets and the unbalances of traffic sign distribution are the key bottlenecks of this research.  Hence, we choose to work with support vector machines (SVM) with a custom-built unbalance dataset, to build a lightweight model with excellent classification accuracy.  The SVM model delivered optimum performance with the radial basis kernel, C=10, and gamma=0.0001. In the proposed method, same priority was given to processing time (testing time) and accuracy, as traffic sign identification is time critical. The final accuracy obtained was 87% (with confidence interval 84%-90%) with a processing time of 0.64s (with confidence interval of 0.57s-0.67s) for correct detection at testing, which emphasizes the effectiveness of the proposed method