Intelligent imaging system for optimal night time driving

In the recent era, vehicles become a need of public. According to Statistic Portal, in year 2018 alone, more than 81 million vehicles were sold. This results in a large number of vehicles commuting on roads, thus increases the risks of road users. Road safety is the paramount and joint responsibility of all road users, which include pedestrians and travellers using different means of transport. Safety is always a main concern for drivers. It is a complex and difficult task even for an experienced senior driver. Road accident is the most unwanted thing to happen to a road user; it was reported that most of the road users are familiar with the general rules and safety measures when using roads, nonetheless their carelessness are causing the accidents and crashes. Zhang et.al [1] proposed an intelligent driver assist system for urban driving. This system provided smart navigation for its users with intelligent parking assistance to improve driving comfort while ensure the safety of the driver. The investigations of the system performance showed high precisions in the determination of the traffic flow and parking availabilit

Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios

The recent advancements in Intelligent Transportation Systems (ITS) have revealed significant potential for enhancing traffic management through Advanced Driver Assist Systems (ADASs), with benefits for both safety and environment. This research paper proposes a vehicle localization technique based on Kalman filtering, as accurate positioning of the ego-vehicle is essential for the proper functioning of the Traffic Light Advisor (TLA) system. The aim of the TLA is to calculate the most suitable speed to safely reach and pass the first traffic light in front of the vehicle and subsequently keep that velocity constant to overcome the following traffic light, thus allowing safer and more efficient driving practices, thereby reducing safety risks, and minimizing energy consumption. To overcome Global Positioning Systems (GPS) limitations encountered in urban scenarios, a multi-rate sensor fusion approach based on the Kalman filter with map matching and a simple kinematic one-dimensional model is proposed. The experimental results demonstrate an estimation error below 0.5 m on urban roads with GPS signal loss areas, making it suitable for TLA application. The experimental validation of the Traffic Light Advisor system confirmed the expected benefits with a 40% decrease in energy consumption compared to unassisted driving

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

Dawn of autonomous vehicles: review and challenges ahead

This paper reviews the state of the art on autonomous vehicles as of 2017, including their impact at socio-economic, energy, safety, congestion and land-use levels. This impact study focuses on the issues that are common denominators and are bound to arise independently of regional factors, such as (but not restricted to) change to vehicle ownership patterns and driver behaviour, opportunities for energy and emissions savings, potential for accident reduction and lower insurance costs, and requalification of urban areas previously assigned to parking. The challenges that lie ahead for carmakers, law and policy makers are also explored, with an emphasis on how these challenges affect the urban infrastructure and issues they create for municipal planners and decision makers. The paper concludes with strengths, weaknesses, opportunities, and threats analysis that integrates and relates all these aspects.info:eu-repo/semantics/publishedVersio

Multi-Lane Perception Using Feature Fusion Based on GraphSLAM

An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data for the algorithm. The information is accumulated and fused using GraphSLAM and the result constitutes the basis for a multilane clothoid model. To allow incorporation of additional information sources, input data is processed in a generic format. Evaluation of the method is performed by comparing real data, collected with an experimental vehicle on highways, to a ground truth map. The results show that ego and adjacent lanes are robustly detected with high quality up to a distance of 120 m. In comparison to serial lane detection, an increase in the detection range of the ego lane and a continuous perception of neighboring lanes is achieved. The method can potentially be utilized for the longitudinal and lateral control of self-driving vehicles