Motorcycle detection for ADAS through camera and V2V communication, a comparative analysis of two modern technologies

Motorcycles are one of the most dangerous means of transportation. Its death toll is higher than in others, due to the inherent vulnerability of motorcycle drivers. The latest strategies in Advanced Driving Assistance Systems (ADAS) are trying to mitigate this problem by applying the advances of modern technologies to the road transport. This paper presents two different approaches on motorcycle protection, based on two of the most modern available technologies in ADAS, i.e. Computer Vision and Vehicle to Vehicle Communication (V2V). The first approach is based on data fusion of Laser Scanner and Computer Vision, providing accurate obstacle detection and localization based on laser scanner, and obstacle classification using computer vision and laser. The second approach is based on ad-hoc V2V technology and provides detection in case of occlusion for visual sensors. Both technologies have been tested in the presented work, and a performance comparison is given. Tests performed in different driving situations allows to measure the performance of every algorithm and the limitations of each of them based on empirical and scientific foundations. The conclusions of the presented work help foster of expert systems in the automotive sector by providing further discussion of the viability and impact from each of these systems in real scenarios

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

Image-Based Lateral Position, Steering Behavior Estimation, and Road Curvature Prediction for Motorcycles

International audienceThis letter presents an image-based approach to simultaneously estimate the lateral position of a powered-two-wheeled vehicle on the road, its steering behavior and predict the road curvature ahead of the motorcycle. This letter is based on the inverse perspective mapping technique combined with a road lanes detection algorithm capable of detecting straight and curved lanes. Then, a clothoid model is used to extract pertinent information from the detected road markers. Finally, the performance of the proposed approach is illustrated through simulations carried out with the well-known motorcycle simulator “BikeSim.” The results are very promising since the algorithm is capable of estimating, in real time, the road geometry and the vehicle location with a better accuracy than the one given by the commercial GPS

Prediction of motorcyclist stress using a heartrate strap, the vehicle telemetry and road information

The number of motorcycles on the road has increased in almost all European countries according to Eurostat. Although the total number of motorcycles is lower than the number of cars, the accident rate is much higher. A large number of these accidents are due to human errors. Stress is one of the main reasons behind human errors while driving. In this paper, we present a novel mechanism to predict upcoming values for stress levels based on current and past values for both the driving behavior and environmental factors. First, we analyze the relationship between stress levels and different variables that model the driving behavior (accelerations, decelerations, positive kinetic energy, standard deviation of speed, and road shape). Stress levels are obtained utilizing a Polar H7 heart rate strap. Vehicle telemetry is captured using a smartphone. Second, we study the accuracy of several machine learning algorithms (Support Vector Machine, Multilayer Perceptron, Naive Bayes, J48, and Deep Belief Network) when used to estimate the stress based on our input data. Finally, an experiment was conducted in a real environment. We considered three different scenarios: home-workplace route, workplace-home route, and driving under heavy traffic. The results show that the proposal can estimate the upcoming stress with high accuracy. This algorithm could be used to develop driving assistants that recommend actions to prevent the stress.The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R funded by the Spanish MINECO, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte and from a sabbatical leave by the Carlos III of Madrid University

Active safety systems for powered two-wheelers: A systematic review

Objective: Active safety systems, of which antilock braking is a prominent example, are going to play an important role to improve powered two-wheeler (PTW) safety. This paper presents a systematic review of the scientific literature on active safety for PTWs. The aim was to list all systems under development, identify knowledge gaps and recognize promising research areas that require further efforts. Methods: A broad search using "safety" as the main keyword was performed on Scopus, Web of Science and Google Scholar, followed by manual screening to identify eligible papers that underwent a full-text review. Finally, the selected papers were grouped by general technology type and analyzed via structured form to identify the following: specific active safety system, study type, outcome type, population/sample where applicable, and overall findings. Results: Of the 8,000 papers identified with the initial search, 85 were selected for full-text review and 62 were finally included in the study, of which 34 were journal papers. The general technology types identified included antilock braking system, autonomous emergency braking, collision avoidance, intersection support, intelligent transportation systems, curve warning, human machine interface systems, stability control, traction control, and vision assistance. Approximately one third of the studies considered the design and early stage testing of safety systems (n. 22); almost one fourth (n.15) included evaluations of system effectiveness. Conclusions: Our systematic review shows that a multiplicity of active safety systems for PTWs were examined in the scientific literature, but the levels of development are diverse. A few systems are currently available in the series production, whereas other systems are still at the level of early-stage prototypes. Safety benefit assessments were conducted for single systems, however, organized comparisons between systems that may inform the prioritization of future research are lacking. Another area of future analysis is on the combined effects of different safety systems, that may be capitalized for better performance and to maximize the safety impact of new technologies

Safe Intelligent Driver Assistance System in V2X Communication Environments based on IoT

In the modern world, power and speed of cars have increased steadily, as traffic continued to increase. At the same time highway-related fatalities and injuries due to road incidents are constantly growing and safety problems come first. Therefore, the development of Driver Assistance Systems (DAS) has become a major issue. Numerous innovations, systems and technologies have been developed in order to improve road transportation and safety. Modern computer vision algorithms enable cars to understand the road environment with low miss rates. A number of Intelligent Transportation Systems (ITSs), Vehicle Ad-Hoc Networks (VANETs) have been applied in the different cities over the world. Recently, a new global paradigm, known as the Internet of Things (IoT) brings new idea to update the existing solutions. Vehicle-to-Infrastructure communication based on IoT technologies would be a next step in intelligent transportation for the future Internet-of-Vehicles (IoV). The overall purpose of this research was to come up with a scalable IoT solution for driver assistance, which allows to combine safety relevant information for a driver from different types of in-vehicle sensors, in-vehicle DAS, vehicle networks and driver`s gadgets. This study brushed up on the evolution and state-of-the-art of Vehicle Systems. Existing ITSs, VANETs and DASs were evaluated in the research. The study proposed a design approach for the future development of transport systems applying IoT paradigm to the transport safety applications in order to enable driver assistance become part of Internet of Vehicles (IoV). The research proposed the architecture of the Safe Intelligent DAS (SiDAS) based on IoT V2X communications in order to combine different types of data from different available devices and vehicle systems. The research proposed IoT ARM structure for SiDAS, data flow diagrams, protocols. The study proposes several IoT system structures for the vehicle-pedestrian and vehicle-vehicle collision prediction as case studies for the flexible SiDAS framework architecture. The research has demonstrated the significant increase in driver situation awareness by using IoT SiDAS, especially in NLOS conditions. Moreover, the time analysis, taking into account IoT, Cloud, LTE and DSRS latency, has been provided for different collision scenarios, in order to evaluate the overall system latency and ensure applicability for real-time driver emergency notification. Experimental results demonstrate that the proposed SiDAS improves traffic safety

Wakabi: on-demand ride service for rural Uganda

In Uganda, the majority of the population lives in rural villages that rely on last-mile distribution for goods such as vaccines, fresh water, trade goods, and other forms of humanitarian relief. Last-mile distribution refers to the last mile (or few miles) that goods must be transported in order to reach their final destination from a main delivery hub. Coordination is one of the primary issues that exist when trying to solve the last-mile problem. In this paper we present our solution to this problem: an SMSbased, on-demand ride-sharing service designed to empower the people of rural Uganda by helping organize the transport of both people and goods. Our application functions similarly to the popular ride-sharing app Uber or Lyft but does not require a smartphone to use. Users text a predefined number to request a ride, get paired with a nearby boda-boda driver (these motorcycle drivers currently offer ride-sharing services to rural Ugandans by word-of-mouth), and are transported to their destination. The service also allows users to specify trailer requirements in case they need to transport goods as well. By building Wakabi around the existing boda-boda system we are not only helping to coordinate last-mile distribution efforts, but are also improving the efficiency of the existing boda-boda drivers that provide transportation to rural Ugandans. Following the 20142015 academic year, Fulbright Scholar and business partner Ty Van Herweg will be responsible for both testing and deploying Wakabi in Uganda. We hope that our application will help boda-boda drivers better serve their riders, and provide businesses with an ideal and cost-effective last-mile distribution solution

Review of current study methods for VRU safety : Appendix 4 –Systematic literature review: Naturalistic driving studies

With the aim of assessing the extent and nature of naturalistic studies involving vulnerable road users, a systematic literature review was carried out. The purpose of this review was to identify studies based on naturalistic data from VRUs (pedestrians, cyclists, moped riders and motorcyclists) to provide an overview of how data was collected and how data has been used. In the literature review, special attention is given to the use of naturalistic studies as a tool for road safety evaluations to gain knowledge on methodological issues for the design of a naturalistic study involving VRUs within the InDeV project. The review covered the following types of studies: •Studies collecting naturalistic data from vulnerable road users (pedestrians, cyclists, moped riders, motorcyclists). •Studies collecting accidents or safety-critical situations via smartphones from vulnerable road users and motorized vehicles. •Studies collecting falls that have not occurred on roads via smartphones. Four databases were used in the search for publications: ScienceDirect, Transport Research International Documentation (TRID), IEEE Xplore and PubMed. In addition to these four databases, six databases were screened to check if they contained references to publications not already included in the review. These databases were: Web of Science, Scopus, Google Scholar, Springerlink, Taylor & Francis and Engineering Village.The findings revealed that naturalistic studies of vulnerable road users have mainly been carried out by collecting data from cyclists and pedestrians and to a smaller degree of motorcyclists. To collect data, most studies used the built-in sensors of smartphones, although equipped bicycles or motorcycles were used in some studies. Other types of portable equipment was used to a lesser degree, particularly for cycling studies. The naturalistic studies were carried out with various purposes: mode classification, travel surveys, measuring the distance and number of trips travelled and conducting traffic counts. Naturalistic data was also used for assessment of the safety based on accidents, safety-critical events or other safety-related aspect such as speed behaviour, head turning and obstacle detection. Only few studies detect incidents automatically based on indicators collected via special equipment such as accelerometers, gyroscopes, GPS receivers, switches, etc. for assessing the safety by identifying accidents or safety-critical events. Instead, they rely on self-reporting or manual review of video footage. Despite this, the review indicates that there is a large potential of detecting accidents from naturalistic data. A large number of studies focused on the detection of falls among elderly people. Using smartphone sensors, the movements of the participants were monitored continuously. Most studies used acceleration as indicator of falls. In some cases, the acceleration was supplemented by rotation measurements to indicate that a fall had occurred. Most studies of using kinematic triggers for detection of falls, accidents and safety-critical events were primarily used for demonstration of prototypes of detection algorithms. Few studies have been tested on real accidents or falls. Instead, simulated falls were used both in studies of vulnerable road users and for studies of falls among elderly people