ON RAIN INFORMATION AS MAP FEATURES FOR CAR NAVIGATION SYSTEMS

In this paper we present a design concept, architecture and implementation of a microservice to process and integrate rain information into a car navigation system in the form of rain map features. Two different input data sources are considered: QuadTile JSON format and GeoTIFF images. Our system converts this input data into an ouput GeoJSON format with only the most relevant information for the map overlay system in the navigation system of the car. We discuss different options for the cloud appearance, like color, shape and transparency. We present our microservices architecture together with data pipelines and implementation. Our approach allows for low latency and spare computing resources, which are especially needed in embedded systems. Finally, we discuss the advantages and disadvantages of our approach as well as further work.

The Path Optimization Algorithm of Car Navigation System considering Node Attributes under Time-Invariant Network

Vehicle path planning plays a key role in the car navigation system. In actual urban traffic, the time spent at intersections accounts for a large proportion of the total time and cannot be ignored. Therefore, studying the shortest path planning problem considering node attributes has important practical significance. In this article, we study the vehicle path planning problem in time-invariant networks, with the minimum travel time from the starting node to the destination node as the optimization goal (including node time cost). Based on the characteristics of the problem, we construct the mathematical model. We propose a Reverse Order Labeling Algorithm (ROLA) based on the traditional Dijkstra algorithm to solve the problem; the correctness of the proposed algorithm is proved theoretically, and we analyse and give the time complexity of the ROLA and design a calculation example to verify the effectiveness of the algorithm. Finally, through extensive simulation experiments, we compare the performance of the proposed ROLA with several other existing algorithms. The experimental results show that the proposed algorithm has good stability and high efficiency.

INTEGRITY – A TOPIC FOR PHOTOGRAMMETRY ?

Photogrammetric methods and sensors like LIDAR, RADAR and cameras are becoming more and more important for new applications like highly automatic driving, since they enable capturing relative information of the ego vehicle w.r.t its environment. Integrity measure the trust that we can put in the navigation information of a system. The concept of integrity was first developed for civil aviation and is linked to reliability concepts well known in geodesy and photogrammetry. Currently, the navigation community is discussing how to guarantee integrity for car navigation and multi-sensor systems.In this paper, we will give a short review on integrity concepts and on the current discussion of how to apply it to car navigation. We will discuss which role photogrammetry could play to solve the open issues in the integrity definition and monitoring for multi-sensor systems.

Classification of Inter-Urban Highway Drivers’ Resting Behavior for Advanced Driver-Assistance System Technologies using Vehicle Trajectory Data from Car Navigation Systems

Fatigue-related crashes, which are mainly caused by drowsy or distracted driving, account for a significant portion of fatal accidents on highways. Smart vehicle technologies can address this issue of road safety to improve the sustainability of transportation systems. Advanced driver-assistance system (ADAS) can aid drowsy drivers by recommending and guiding them to rest locations. Past research shows a significant correlation between driving distance and driver fatigue, which has been actively studied in the analysis of resting behavior. Previous research efforts have mainly relied on survey methods at specific locations, such as rest areas or toll booths. However, such traditional methods, like field surveys, are expensive and often produce biased results, based on sample location and time. This research develops methods to better estimate travel resting behavior by utilizing a large-scale dataset obtained from car navigation systems, which contain 591,103 vehicle trajectories collected over a period of four months in 2014. We propose an algorithm to statistically categorize drivers according to driving distances and their number of rests. The main algorithm combines a statistical hypothesis test and a random sampling method based on the renowned Monte-Carlo simulation technique. We were able to verify that cumulative travel distance shares a significant relationship with one’s resting decisions. Furthermore, this research identifies the resting behavior pattern of drivers based upon their travel distances. Our methodology can be used by sustainable traffic safety operators to their driver guiding strategies criterion using their own data. Not only will our methodology be able to aid sustainable traffic safety operators in constructing their driver guidance strategies criterion using their own data, but it could also be implemented in actual car navigation systems as a mid-term solution. We expect that ADAS combined with the proposed algorithm will contribute to improving traffic safety and to assisting the sustainability of road systems.