Vehicle Location Research Articles

Highly robust and accurate multi-sensor fusion localization system for complex and challenging scenarios

Highly robust and accurate localization using on-board sensors in the absence of GNSS is a key issue for long-term autonomous navigation of vehicles. However, the limited sensing capabilities of a single sensor cannot resist the effects of feature degradation and high-speed motion in complex and challenging scenarios, which results in the fragile of existing LiDAR-based and visual-based localization algorithms. To solve these problems, we propose a highly robust and accurate localization system by fusing information from multi-sensors. Firstly, we propose a global localization method that leverages deep learning models for processing visual information to provide an accurate initial position for vehicles, which solves the problem of global localization of vehicles in the absence of GNSS. Then, we propose a highly robust and accurate LiDAR-Vision-IMU localization method based on pose graph optimization, which integrates LiDAR-based poses and visual-inertial-based poses. The proposed method detects sensor degradation in real time to dynamically adjust the fusion weights, which solves the problem of low localization performance of a single sensor. Finally, we conduct experiments in complex and challenging scenarios, such as urban, tunnels, and unstructured mountains. Experimental results show that our method can achieve a global localization error of less than 0.5 m in complex scenes. In unstructured mountainous scenarios of length 21.1 km, the root mean square errors (RMSE) of translation and rotation are 0.67 m and 0.048rad, respectively. Compared to most existing algorithms, our method achieves the most robust and accurate localization performance.

Characterizing spatiotemporal patterns of bus bunching frequency on a bus route network: A case study of Taipei city

Bus bunching is a critical issue in bus operation management. This study investigated the impacts of potential factors affecting bus bunching on a city-wide bus route network, using Taipei City as an example with its 267 bus routes and 4741 bus stops. Frequency of bus bunching was measured on an hourly basis using Automatic Vehicle Location data. Multilevel count models based on Conway-Maxwell Poisson distributions were developed, and the spatiotemporal distributions of random intercepts and residuals were examined. The results showed that the effects of bus lane design, bus route network and operation, and traffic characteristics were consistent with those reported in prior studies. However, a quadratic relationship between bus route length, best measured by number of stops, and bus bunching was identified, which explains the conflicts between the theories of bus bunching propagation and self-repair. Bus route design often faces a dilemma between extending route length to collect additional passengers and reducing in-vehicle travel time to enhance service quality. The findings of the study could be a useful reference for bus route design to minimize bus bunching.

A Vision/Inertial Navigation/Global Navigation Satellite Integrated System for Relative and Absolute Localization in Land Vehicles.

This paper presents an enhanced ground vehicle localization method designed to address the challenges associated with state estimation for autonomous vehicles operating in diverse environments. The focus is specifically on the precise localization of position and orientation in both local and global coordinate systems. The proposed approach integrates local estimates generated by existing visual-inertial odometry (VIO) methods into global position information obtained from the Global Navigation Satellite System (GNSS). This integration is achieved through optimizing fusion in a pose graph, ensuring precise local estimation and drift-free global position estimation. Considering the inherent complexities in autonomous driving scenarios, such as the potential failures of a visual-inertial navigation system (VINS) and restrictions on GNSS signals in urban canyons, leading to disruptions in localization outcomes, we introduce an adaptive fusion mechanism. This mechanism allows seamless switching between three modes: utilizing only VINS, using only GNSS, and normal fusion. The effectiveness of the proposed algorithm is demonstrated through rigorous testing in the Carla simulation environment and challenging UrbanNav scenarios. The evaluation includes both qualitative and quantitative analyses, revealing that the method exhibits robustness and accuracy.

Data-driven traffic signal adaptive control algorithm integrating vehicle perception and traffic flow data

Due to the equipment error, environmental interference and data transmission delay of vehicle flow detection, the accuracy and real-time performance of vehicle perception and traffic flow data will be affected to some extent, resulting in poor traffic signal control effect. Therefore, a data-driven traffic signal adaptive control algorithm is designed by integrating vehicle perception and traffic flow data. To complete the modeling of urban traffic, the discrete distribution and continuous distribution of traffic are obtained. Based on this research environment, the DV-hop localization algorithm is improved to sense the vehicle position. Based on the phase space reconstruction of traffic flow time series and vehicle location information, traffic flow data is predicted. Based on the driving of traffic data, the vehicle types are divided into small, medium and large three categories, and the impact weights are assigned respectively, and the weight values affecting the final allocation of green time are obtained to realize the allocation of green time. The experimental results show that: The research algorithm can not only predict the traffic flow intensity effectively, but also the predicted results are highly coincident with the actual traffic flow intensity. Vehicle arrival rates are higher, vehicle delays are shorter, and vehicles stop fewer times on average.

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

A group key agreement protocol for Vanet based on Chinese remainder theorem and blockchain

AbstractVehicular ad hoc Network (VANET) is a wireless self‐organizing network for obtaining information about road conditions, vehicle speed, vehicle location and traffic congestion. Traditional key negotiation protocols create many problems when dealing with a group of vehicles that need to communicate over a public channel. For example, traditional key negotiation protocols rely too much on the participation of trusted institutions and suffer from a single point of failure. Meanwhile, group session key negotiation is usually inefficient with high computational cost and communication overhead. To solve these problems, this paper proposes a blockchain‐based Chinese Remainder Theorem (CRT) VANET group key agreement. In addition, the protocol supports dynamic management of vehicles, including joining and exit. Formal security proofs show that our solution satisfies basic security requirements. Experiments using ProVerif show that the protocol functions properly even under many active and passive attacks, such as eavesdropping attacks. Performance analysis shows that the protocol is more efficient in the face of multi‐vehicle communication, in particular by reducing the computational cost by up to 75% and the communication overhead by up to 66%.

Optimal Location and Pricing of Electric Vehicle Charging Stations Using Machine Learning and Stackelberg Game

Optimal Location and Pricing of Electric Vehicle Charging Stations Using Machine Learning and Stackelberg Game

Environmental-structure-perception-based adaptive pose fusion method for LiDAR-visual-inertial odometry

Environmental-structure-perception-based adaptive pose fusion method for LiDAR-visual-inertial odometry

Multiple Table Models Based on Queuing Theory for Firefighting Traffic Problems

With the development of global urbanization, urban planning has been a hot spot of most concern. Since the traditional urban fire station setting method has been gradually unable to meet the emergency needs of a city. In this paper, a series of methods are developed to achieve the best balance between economic expenditure and reliability. According to the heat maps about emergency calls in a city in recent years, different types of alarm phones have obvious regional distribution characteristics. Inspired by the Queuing Theory algorithm, we abstract this complex problem into a parallel multi-server waiting queuing model to find each average queue length of three kinds of alarm calls and success in converting the multiplicity problem into solving the vehicle number and location problems respectively. Based on this planning problem, new combinations with different numbers of vehicles can be gotten. Then based on the assignment model, the optimal deployment plan can also be different with different numbers and geographical locations of the three kinds of stations. Our method significantly improves the firefighting traffic problems and can be applied in the actual situation.

SD-SLAM: A semantic SLAM approach for dynamic scenes based on LiDAR point clouds

Point cloud maps generated via LiDAR sensors using extensive remotely sensed data are commonly used by autonomous vehicles and robots for localization and navigation. However, dynamic objects contained in point cloud maps not only downgrade localization accuracy and navigation performance but also jeopardize the map quality. In response to this challenge, we propose in this paper a novel semantic SLAM approach for dynamic scenes based on LiDAR point clouds, referred to as SD-SLAM hereafter. The main contributions of this work are in three aspects: 1) introducing a semantic SLAM framework dedicatedly for dynamic scenes based on LiDAR point clouds, 2) employing semantics and Kalman filtering to effectively differentiate between dynamic and semi-static landmarks, and 3) making full use of semi-static and pure static landmarks with semantic information in the SD-SLAM process to improve localization and mapping performance. To evaluate the proposed SD-SLAM, tests were conducted using the widely adopted KITTI odometry dataset. Results demonstrate that the proposed SD-SLAM effectively mitigates the adverse effects of dynamic objects on SLAM, improving vehicle localization and mapping performance in dynamic scenes, and simultaneously constructing a static semantic map with multiple semantic classes for enhanced environment understanding.

Vehicle Renting System

Abstract: The development of a vehicle rental system aims to provide customers worldwide with the convenience of booking vehicles online. This integrated system automates manual processes, allowing customers to easily input their vehicle preferences and location. The user-friendly interface of the Super vehicle Rental System enhances user experience, making it simple for customers to interact with the platform. Administrators have the capability to manage rentals, bookings, customer inquiries, and vehicle information efficiently. Customers can access vehicle models, descriptions, and prices, register, and view rental plans through a responsive website accessible on various devices. The system, built using PHP, DBMS, and XAMPP, offers a comprehensive solution for vehicle rental services, ensuring a seamless experience for both customers and administrators.

Smart Driver Assistance Using Augmented Reality

Abstract: Augmented Reality (AR) technology enables users to monitor the real world through augmented glasses. The objective of this work is real-time monitoring of the instant location of vehicles, contributing to smart driver assistance. Through this effort, there is a possibility to prevent accidents by displaying real-time data on the Liquid Crystal Display (LCD) Unit via the Augmented glasses. Hence, this endeavor is beneficial for protecting the lives of the drivers as well as the vehicles.

Impact of Station Design and Passengers Flow on Urban Rail Dwell Time: A Systemwide Analysis Using APC and AVL Data

In this paper, we investigated the impact on dwell time of station design, train load, and passenger flow, considering boarding fraction. We employed system-wide automated data sources including automatic passenger counting and automatic vehicle location data in the rail transit system in Calgary, Canada. Regression analyses were conducted to achieve accurate dwell time estimation by calculating passenger load per car, boarding, and alighting passengers per door, and also identifying the critical door with the highest demand. Observations for each critical door were divided based on the fraction of boarding passengers with respect to the sum of boarding and alighting. Six stations with distinct geometric designs were selected for comparison to assess their impact on dwell time. The results indicate that, for dominant boarding or alighting, a longer time is needed per passenger to alight or board, respectively. Our findings indicate that a station with a middle platform and two entrances positioned in the middle performed better in terms of dwell time in the case of alighting-dominant and mixed passenger flow. For stations experiencing boarding-dominant passenger flows, side platforms with multiple entrances at the ends and middle outperformed. Narrower platforms experienced significantly longer dwell times than other selected stations under similar demand. While the conclusion about the station design may not be generic, the proposed model provides a consistent and adaptable approach to study the impact of station design and passenger flows on urban rail dwell times, facilitating better-informed decision-making for station design or modification and enhancement of the overall rail system performance.

Bi-directional information guidance network for UAV vehicle detection

UAV vehicle detection based on convolutional neural network exits a key problem of information imbalance of different feature layers. Shallow features have spatial information that is beneficial to localization, but lack semantic information. On the contrary, deep features have semantic information that is beneficial to classification, but lack spatial information. However, accurate classification and localization for UAV vehicle detection require both shallow spatial information and high semantic information. In our work, a bi-directional information guidance network (BDIG-Net) for UAV vehicle detection is proposed, which can ensure that each feature prediction layer has abundant mid-/low-level spatial information and high-level semantic information. There are two main parts in the BDIG-Net: shallow-level spatial information guidance part and deep-level semantic information guidance part. In the shallow-level guidance part, we design a feature transform module (FTM) to supply the mid-/low-level feature information, which can guide the BDIG-Net to enhance detailed and spatial features for deep features. Furthermore, we adopt a light-weight attention module (LAM) to reduce unnecessary shallow background information, making the network more focused on small-sized vehicles. In the deep-level guidance part, we use classical feature pyramid network to supply high-level semantic information, which can guide the BDIG-Net to enhance contextual information for shallow features. Meanwhile, we design a feature enhancement module (FEM) to suppress redundant features and improve the discriminability of vehicles. The proposed BDIG-Net can reduce the information imbalance. The experimental results show that the BDIG-Net can achieve accurate classification and localization for UAV vehicles and realize the real-time application requirements.

Simple analytical models for estimating the queue lengths from probe vehicles at traffic signals: A combinatorial approach for nonparametric models

This study develops a combinatorial approach for nonparametric short-term queue length estimation in terms of cycle-by-cycle partially observed queues from probe vehicles (PV). The method does not assume random arrivals and does not assume any primary parameters or estimation of any parameters but uses simple algebraic expressions that only depend on signal timing. For an approach lane at a traffic intersection, the conditional queue lengths given probe vehicle location, count, time, and analysis interval (e.g., at the end of the red signal phase) are represented by a Negative Hypergeometric distribution. The simple analytical estimators obtained are compared with parametric methods from literature and highway capacity manual methods using field test data and simulation data involving probe vehicles. The analysis indicates that the nonparametric models presented in this paper match the accuracy of the parametric ones used in the field test and simulated data for estimating queue lengths.

Drive Time Vehicle Breakdown Assistance

Drive Time Vehicle Breakdown Assistance is a user-centric platform dedicated to providing swift and efficient assistance to drivers experiencing vehicle issues. Through seamless chatbot integration, users can easily connect with mechanics in their vicinity, ensuring prompt resolution of breakdowns tailored to their vehicle type and location. Additionally, the platform offers a comprehensive tutorial section empowering users to tackle common issues independently, fostering a sense of self-sufficiency. Moreover, users can seamlessly search for nearby petrol stations from their location, ensuring they have access to essential services during their journeys. In summary, Drive Time Vehicle Breakdown Assistance is more than just a platform—it's a comprehensive solution designed to provide peace of mind to drivers, ensuring safer and more convenient journeys on the road. Keywords- assistance, breakdowns, chatbot, drivers, efficient, independence, mechanics, petrol, platform, tutorial

Automatic Calibration of Environmentally Installed 3D-LiDAR Group Used for Localization of Construction Vehicles

Automatic Calibration of Environmentally Installed 3D-LiDAR Group Used for Localization of Construction Vehicles

Reliable urban vehicle localization under faulty satellite navigation signals

Reliable urban navigation using global navigation satellite system requires accurately estimating vehicle position despite measurement faults and monitoring the trustworthiness (or integrity) of the estimated location. However, reflected signals in urban areas introduce biases (or faults) in multiple measurements, while blocked signals reduce the number of available measurements, hindering robust localization and integrity monitoring. This paper presents a novel particle filter framework to address these challenges. First, a Bayesian fault-robust optimization task, formulated through a Gaussian mixture model (GMM) measurement likelihood, is integrated into the particle filter to mitigate faults in multiple measurement for enhanced positioning accuracy. Building on this, a novel test statistic leveraging the particle filter distribution and the GMM likelihood is devised to monitor the integrity of the localization by detecting errors exceeding a safe threshold. The performance of the proposed framework is demonstrated on real-world and simulated urban driving data. Our localization algorithm consistently achieves smaller positioning errors compared to existing filters under multiple faults. Furthermore, the proposed integrity monitor exhibits fewer missed and false alarms in detecting unsafe large localization errors.

Side Collision Detection Model for Visually Impaired Using Monocular Object-specific Distance Estimation and Multimodal Real-World Location Calculation

Targeting the potential risk of side-vehicle collisions when the visually impaired crosses roads, this study proposed a side collision detection model, including monocular distance estimation, multimodal real-world location estimation, future location prediction and collision warning strategies tailored for visually impaired pedestrians. The proposed model employs YOLOv8 and DeepSort for vehicle detection and tracking, utilizing shallow neural networks for distance estimation based on image information and vehicle position data. Predicted vehicle distances are combined with magnetic field sensor and GPS data to compute and store real-world vehicle locations, and these location data will be used for linear regression to forecast future locations. A warning strategy is then implemented to alert users. Experimental validation shows that the monocular distance estimation network has an Absolute Relative Error of 0.043 and an ALE (Average Localization Error) of 1.249m. In real-world location estimation, the view angle ALE is 0.019, and the location ALE is 1.778m. Regarding location prediction, the accuracy in distinguishing stationary and moving vehicles reaches 0.962, and the predicted curve, based on ground truth and predicted locations, exhibits good alignment. The proposed warning strategy, evaluated on Kitti Tracking Dataset and a self-created dataset, accurately detects the majority of potential collision risks.

Magnetic Localization Method for Vehicles Based on Transformer

Magnetic positioning is a promising technique for vehicles in Global Navigation Satellite System (GNSS)-denied scenarios. Traditional magnetic positioning methods resolve the position coordinates by calculating the similarity between the measured sequence and the sequence generated from the magnetic database with criteria such as the Mean Absolute Difference (MAD), PRODuct correlation (PROD), etc., which usually suffer from a high mismatch rate. To solve this problem, we propose a novel magnetic localization method for vehicles based on Transformer. In this paper, we cast the magnetic localization problem as a regression task, in which a neural network is trained by equidistant sequences to predict the current position. In addition, by adopting Transformer to perform magnetic localization of vehicles for the first time, magnetic features are extracted, and positional relationships are explored to guarantee positioning accuracy. The experimental results show that the proposed method can greatly improve the magnetic positioning accuracy, with an average improvement of approximately 2 m.