Real-time Road Information Research Articles

A Dynamic and Scalable User-Centric Route Planning Algorithm Based on Polychromatic Sets Theory

Existing navigation services provide route options based on a single metric without considering user’s preference. This results in the planned route not meeting the actual needs of users. In this paper, a personalized route planning algorithm is proposed, which can provide users with a route that meets their requirements. Based on the multiple properties of the road, the Polychromatic Sets (PS) theory is introduced into route planning. Firstly, a road properties description scheme based on the PS theory was proposed. By using this scheme, users’ travel preferences can be quantified, and then personalized property combination schemes can be constructed according to these properties. Secondly, the idea of setting priority for road segments was utilized. Based on a user’s travel preference, all the property combination schemes can be prioritized at relevant levels. Finally, based on the priority level, an efficient path planning scheme was proposed, in which priority is given to the highest road segments in the target direction. In addition, the system can constantly obtain real-time road information through mobile terminals, update road properties, and provide other users with more accurate road information and navigation services, so as to avoid crowded road segments without excessively increasing time consumption. Experiment results show that our algorithm can realize personalized route planning services without significantly increasing the travel time and distance. In addition, source code of the algorithm has been uploaded on GitHub for this algorithm to be used by other researchers.

FPGA-based vehicle real-time path planning system

In order to realize the requirements of low latency and low power consumption in real-time path planning for private vehicles, this paper designs an FPGA accelerator based on real-time road information and Dijkstra's shortest path algorithm, which is applied to the path planning system of vehicle edge computing. The system is designed using Xilinx High-Level Synthesis (HLS) compiler and is implemented in the programming logic of a Xilinx Zynq FPGA. The experiment is carried out on a city map, and the results show that the system has lower circuit area and power consumption, and its computing performance is 3.8 times that of ARM, which is suitable for edge computing platform.

Semi-Direct Monocular Visual-Inertial Odometry Using Point and Line Features for IoV

The precise measuring of vehicle location has been a critical task in enhancing the autonomous driving in terms of intelligent decision making and safe transportation. Internet of Vehicles ( IoV ) is an important infrastructure in support of autonomous driving, allowing real-time road information exchanging and sharing for localizing vehicles. Global positioning System ( GPS ) is widely used in the traditional IoV system. GPS is unable to meet the key application requirements of autonomous driving due to meter level error and signal deterioration. In this article, we propose a novel solution, named Semi-Direct Monocular Visual-Inertial Odometry using Point and Line Features ( SDMPL-VIO ) for precise vehicle localization. Our SDMPL-VIO model takes advantage of a low-cost Inertial Measurement Unit ( IMU ) and monocular camera, using them as the sensor to acquire the surrounding environmental information. Visual-Inertial Odometry ( VIO ), taking into account both point and line features, is proposed, which is able to deal with both weak texture and dynamic environment. We use a semi-direct method to deal with keyframes and non-keyframes, respectively. Dual sliding window mechanisms can effectively fuse point-line and IMU information. To evaluate our SDMPL-VIO system model, we conduct extensive experiments on both an indoor dataset (i.e., EuRoC) and an outdoor dataset (i.e., KITTI) from the real-world applications, respectively. The experimental results show that the accuracy of SDMPL-VIO proposed by us is better than the mainstream VIO system at present. Especially in the weak texture of the datasets, fast-moving datasets, and other challenging datasets, SDMPL-VIO has a relatively high robustness.

Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle.

Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.

Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios

This paper proposes a bi-level eco-driving control strategy for connected and automated hybrid electric vehicles (CAHEVs) under mixed driving scenarios. First, the hybrid electric vehicle powertrain is modelled, and the communications via Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) are introduced as the main data sources for the decision-making of the control system. Next, the problem is divided into three objectives, namely, (1) safe driving, (2) energy management, and (3) exhaust emission reduction. Based on the real-time road information, the driving scenario classifier (DSC) works towards determining the corresponding vehicle mode on which the cost function can be adjusted accordingly. The simulation is carried out in a realistic urban traffic simulation environment in SUMO. The results show that with the proposed model predictive control (MPC)-based strategy applied, safe driving in a trip involving a mixture of driving scenarios can be guaranteed throughout the entire driving. In addition, in comparison to the rule-based benchmark strategy, the proposed strategy can reduce the fuel consumption by 34.10% with battery kept in a healthy state of charge range, and the exhaust emissions (HC, CO, and NOx) are reduced by 25.36%, 72.30%, and 30.39%, respectively, which demonstrates the effectiveness and robustness of the proposed MPC-based strategy for CAHEVs.

Pose Prediction of Autonomous Full Tracked Vehicle Based on 3D Sensor.

Autonomous vehicles can obtain real-time road information using 3D sensors. With road information, vehicles avoid obstacles through real-time path planning to improve their safety and stability. However, most of the research on driverless vehicles have been carried out on urban even driveways, with little consideration of uneven terrain. For an autonomous full tracked vehicle (FTV), the uneven terrain has a great impact on the stability and safety. In this paper, we proposed a method to predict the pose of the FTV based on accurate road elevation information obtained by 3D sensors. If we could predict the pose of the FTV traveling on uneven terrain, we would not only control the active suspension system but also change the driving trajectory to improve the safety and stability. In the first, 3D laser scanners were used to get real-time cloud data points of the terrain for extracting the elevation information of the terrain. Inertial measurement units (IMUs) and GPS are essential to get accurate attitude angle and position information. Then, the dynamics model of the FTV was established to calculate the vehicle’s pose. Finally, the Kalman filter was used to improve the accuracy of the predicted pose. Compared to the traditional method of driverless vehicles, the proposed approach was more suitable for autonomous FTV. The real-world experimental result demonstrated the accuracy and effectiveness of our approach.

When Road Information Meets Data Mining: Precision Detection for Heading and Width of Roads

Real-time road information plays a crucial role in enabling intelligent transportation systems (ITS) applications. With sufficient road information, the map of road topography can be built and updated more easily. Furthermore, many appealing ITS applications can be enabled accordingly. Aiming at improving the quality and update rate of road information, a hot topic today is how to mine information from global positioning systems (GPS) trajectories by the clustering-based methods. Such schemes, however, encounter two challenges: 1) GPS noise and 2) low sampling rate of GPS traces data. As a result, it is difficult to infer road information from these irregular clusters. To tackle the above issues, we directly mine useful road information, heading, and width of roads, for ITS applications from GPS point cloud, i.e., a set of GPS points. First, the distribution of GPS points is discussed and the least squares method (LSM) is demonstrated to be outstanding for mining the heading of the road under a huge number of GPS points. Second, the weighted approximation least squares method is proposed to improve the accuracy of the LSM. Furthermore, combining with relevant distribution features in GPS points, the data distribution variance-road width discrete model is proposed to mine road width from GPS point cloud. Finally, using real-world datasets, we demonstrate that these proposed methods can achieve satisfactory performance in practice.

Secure Certificateless Authentication and Road Message Dissemination Protocol in VANETs

As a crucial component of Internet‐of‐Thing (IoT), vehicular ad hoc networks (VANETs) have attracted increasing attentions from both academia and industry fields in recent years. With the extensive VANETs deployment in transportation systems of more and more countries, drivers’ driving experience can be drastically improved. In this case, the real‐time road information needs to be disseminated to the correlated vehicles. However, due to inherent wireless communicating characteristics of VANETs, authentication and group key management strategies are indispensable for security assurance. Furthermore, effective road message dissemination mechanism is of significance. In this paper, we address the above problems by developing a certificateless authentication and road message dissemination protocol. In our design, certificateless signature and the relevant feedback mechanism are adopted for authentication and group key distribution. Subsequently, message evaluating and ranking strategy is introduced. Security analysis shows that our protocol achieves desirable security properties. Additionally, performance analysis demonstrates that the proposed protocol is efficient compared with the state of the art.

An Effective Recognition Method for Road Information Based on Mobile Terminal

This paper describes a design of fast recognition of road information based on mobile terminal. Firstly, based on the HOG algorithm, we study and verify the effects of different parameters on the performance of the algorithm. Secondly, we test 800 images randomly selected from the INRIA pedestrian dataset to obtain the optimal parameters for the mobile terminal and the proportion of video resolution and detection window. Then, under the same test conditions, the time overheads of the SVMLight and the LibSVM are recorded and SVMLight training time is significantly less than LibSVM. Thirdly, we design and implement a real-time road information recognition and warning system on the Windows platform and Android platform. Features include real-time pedestrians detection, voice warning, and road signs recognition. When the vehicle speed is less than 30 km/h, the video resolution is less than 720 × 576 and the detection window/image ratio is less than 1 : 50; the system can guarantee low delay and high recognition rate (97.2%).

Real-Time Road Traffic Management Using Floating Car Data

Information and communication technology (ICT) is a promising solution for mitigating road traffic congestion. ICT allows road users and vehicles to be managed based on real-time road status information. In Tanzania, traffic congestion causes losses of TZS 655 billion per year. The main objective of this study was to develop an optimal approach for integrating real-time road information (RRI) to mitigate traffic congestion. Our research survey focused on three cities that are highly affected by traffic congestion, i.e., Arusha, Mwanza, and Dar es Salaam. The results showed that ICT is not yet utilized fully to solve road traffic congestion. Thus, we established a possible approach for Tanzania based on an analysis of road traffic data provided by organizations responsible for road traffic management and road users. Furthermore, we evaluated the available road information management techniques to test their suitability for use in Tanzania. Using the floating car data technique, fuzzy logic was implemented for real-time traffic level detection and decision making. Based on this solution, we propose a RRI system architecture, which considers the effective utilization of readily available communication technology in Tanzania.

Variable Speed Limit to Improve Safety near Traffic Congestion on Urban Freeways

Recently, the convergence of information technology with biotechnology, nano-technology, or other technologies has been creating a new paradigm. In the field of transportation, intelligent transport systems (ITSs)-a convergence of information technologies and transportation systems-have been studied. The VSL is one ITS technologies that aims to improve the safety and efficiency of transportation while controlling the speed limit according to traffic circumstances. Existing studies for VSL algorithms have considered only one station to control the traffic. However, it is not appropriate for an urban freeway to be installed with many stations. In this paper, a new VSL algorithm is proposed to enhance the effectiveness of VSL for multiple stations. It is based on the cooperation of stations and the real-time road information. The proposed algorithm consists of 4 steps: first is a ”searching bottleneck station,” second is a ”calculating a size of congestion,” third is a ”calculating the number of controlled stations,” fourth is a ”calculating VSL.” In our experiments, the microscopic traffic simulator VISSIM performed our modeling works. The results show that the proposed algorithm improves safety on roads with minimum additional travel time.

Intelligent Vehicular Navigation System Based on Real-time Road Information

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.