Vehicles In Urban Environments Research Articles

Pedestrian safety on the road to net zero: cross-sectional study of collisions with electric and hybrid-electric cars in Great Britain

BackgroundPlans to phase out fossil fuel-powered internal combustion engine (ICE) vehicles and to replace these with electric and hybrid-electric (E-HE) vehicles represent a historic step to reduce air pollution and...

Flowfield Analysis of Vortex Interactions During Large Sharp-Edged Gusts

Uncrewed air vehicles in urban environments will have flights during terminal operations that are dominated by strong transient aerodynamics. These vehicles are not only lighter and smaller than traditional rotorcraft and helicopters, but in many instances they may be hybrid configurations with lifting surfaces similar to those of fixed-wing aircraft. These differences require further understanding of the physics of these transient aerodynamics, specifically large-amplitude transverse gusts and the resulting vehicle response, where classic indicial theory is no longer valid. This is crucial to the safety and certification of these air vehicles near buildings and populations. Prior efforts identified that gust responses depart from traditional linear theory when the leading-edge vortex (LEV) forms as a distinct feature and departs from the lifting surface, resulting in flow nonlinearities. This paper expands our understanding of the interactional physics of these nonlinear transverse gusts with flow separation. LEV and trailing-edge vortex (TEV) behavior are correlated, and these vortex interactions are studied to understand the impact on flow separation and subsequent aerodynamic behavior. Larger gust ratios are observed to increase the LEV normal translation, while flow separation is driven by the location and magnitude of the TEV.

Estimating coverage and capacity of high frequency mobile networks in ultradense urban areas

High frequency communications (mmWave and TeraHz) in urban areas require a higher density of base stations compared to pre-5G mobile networks, but open the way to a quantum leap in increased throughput and reduced latency. However, we currently have no indication of how much we need to densify the deployment, and on the trade-off between the density of base stations and the performance improvement. This paper studies the problem of base stations placement to guarantee coverage to vehicles and pedestrians in urban areas when using high frequency communications. Our novel methodology takes advantage of vehicular traffic simulations and precise urban maps to generate a realistic demand model for vehicles and pedestrians in urban areas. We use a bounded error heuristic to find the maximal coverage that can be achieved with a given density of base stations, primarily using line-of-sight communications. We implemented the heuristic using Cuda libraries on Nvidia GPUs and evaluated the coverage in an urban area in the city of Luxembourg, for which vehicular traffic patterns are available. We focus on coverage and capacity analysis for the mmWave frequency, but the results are easily extended to TeraHz communications. Our results are the first to show that a reasonably low density (15 base stations per km2) is sufficient to provide coverage for vehicles in urban environments. However, optimizing on vehicles or on pedestrians are competing objectives: the operator needs to choose which one to target based on its business model when designing the network infrastructure. Our algorithms, code and open data can be used to perform this task and reproduce our results in different settings.

Method for Estimating Road Impulse Ahead of Vehicles in Urban Environment with Microelectromechanical System Three-Dimensional Sensor.

Most automated vehicles (AVs) are equipped with abundant sensors, which enable AVs to improve ride comfort by sensing road elevation, such as speed bumps. This paper proposes a method for estimating the road impulse features ahead of vehicles in urban environments with microelectromechanical system (MEMS) light detection and ranging (LiDAR). The proposed method deploys a real-time estimation of the vehicle pose to solve the problem of sparse sampling of the LiDAR. Considering the LiDAR error model, the proposed method builds the grid height measurement model by maximum likelihood estimation. Moreover, it incorporates height measurements with the LiDAR error model by the Kalman filter and introduces motion uncertainty to form an elevation weight method by confidence eclipse. In addition, a gate strategy based on the Mahalanobis distance is integrated to handle the sharp changes in elevation. The proposed method is tested in the urban environment. The results demonstrate the effectiveness of our method.

A LiDAR–INS-aided geometry-based cycle slip resolution for intelligent vehicle in urban environment with long-term satellite signal loss

A LiDAR–INS-aided geometry-based cycle slip resolution for intelligent vehicle in urban environment with long-term satellite signal loss

Towards comprehensive understanding of pedestrians for autonomous driving: Efficient multi-task-learning-based pedestrian detection, tracking and attribute recognition

Safe interaction with pedestrians is important for autonomous vehicles in urban environments. Comprehensive understanding of pedestrian behavior is a challenge for autonomous driving, as different pedestrian attributes have different characteristics and there is not yet a dataset providing all attribute labels. In this paper, we design an efficient multi-task model for pedestrian detection, tracking and attribute recognition to fully mine information in multiple tasks and multiple datasets to reduce redundant image processing computation. Multiple training datasets are utilized to take full advantage of the available partially-labeled data. The model consists of two stages. The first stage is to detect and track pedestrians, using only low-resolution images to save computational resources. Accurate pedestrian detection is achieved in the bird's-eye-view (BEV), while pedestrian tracking is implemented on the basis of 3D bounding boxes. The second stage is image-based multi-attribute detection. This stage uses the detection and tracking results and some pre-processed information from the first stage along with high-resolution images. Multi-attributes of the pedestrian are identified, such as pedestrian's pose, age, gender, and motion direction. With a carefully-designed multi-task system that shares necessary feature maps among multiple tasks and reuses computation results, we can solve pedestrian detection, tracking, and multi-attribute recognition problems in an efficient framework. This leads to more accurate decisions and more efficient computations. The innovation of the model is to accomplish a simultaneous detection of multiple pedestrian attributes for autonomous driving, while fully sharing the features in the model to avoid repeated computation, as well as utilizing multiple partially-labeled datasets for the pedestrian detection, tracking and attribute recognition model training.

A whole-net cooperative positioning method based on CDGNSS and inter-vehicle ranging

Relative positioning is recognized as an important issue for vehicles in urban environments. Multi-vehicle Cooperative Positioning (CP) techniques which fuse the Global Navigation Satellite System (GNSS) and inter-vehicle ranging have attracted attention in improving the performance of baseline estimation between vehicles. However, current CP methods estimate the baselines separately and ignore the interactions among the positioning information of different baselines. These interactions are called ‘information coupling’. In this work, we propose a new multi-vehicle precise CP framework using the coupled information in the network based on the Carrier Differential GNSS (CDGNSS) and inter-vehicle ranging. We demonstrate the benefit of the coupled information by deriving the Cramer-Rao Lower Bound (CRLB) of the float estimation in CP. To fully use this coupled information, we propose a Whole-Net CP (WN-CP) method which consists of the Whole-Net Extended Kalman Filter (WN-EKF) as the float estimation filter, and the Partial Baseline Fixing (PBF) as the ambiguity resolution part. The WN-EKF fuses the measurements of all baselines simultaneously to improve the performance of float estimation, and the PBF strategy fixes the ambiguities of the one baseline to be estimated, instead of full ambiguity resolution, to reduce the computation load of ambiguity resolution. Field tests involving four vehicles were conducted in urban environments. The results show that the proposed WN-CP method can achieve better performance and meanwhile maintain a low computation load compared to the existing methods.

Energy optimal 3D flight path planning for unmanned aerial vehicle in urban environments

This paper presents a general approach to compute energy optimal flight paths for unmanned aerial vehicle (UAV) in urban environments. To minimize the energy required, the flight path is optimized by exploiting local wind phenomena, i.e., upwind and tailwind areas from the airflow around buildings. A realistic wind field of a model urban environment typical for continental Europe is generated using PALM, a Large Eddy Simulation tool. The calculated wind field feeds into the flight path planning algorithm to minimize the energy required. A specifically tailored A-Star-Algorithm is used to optimize flight trajectories. The approach is demonstrated on a delivery UAV benchmark scenario. Energy optimal flight paths are compared to shortest way trajectories for 12 different scenarios. It is shown that energy can be saved significantly while flying in a city using knowledge of the current wind field.

Fast and deterministic (3+1)DOF point set registration with gravity prior

Point set registration is the technology used to estimate the spatial transformation between two LiDAR scans, which is challenging in the presence of outlier correspondences and noise. Our focus is on 4 degrees of freedom (DOF) point set registration, in which 1DOF rotation and 3DOF translation need to be estimated. It is commonly found in practical scenarios, such as arbitrarily mobile robots equipped with an inertial measurement unit (IMU), terrestrial LiDAR scanners, or planar moving vehicles in urban environments. Recently, many solutions have leveraged branch and bound (BnB) in global and deterministic approaches to solve the registration problem with performance guarantees. However, BnB-based methods are usually time-consuming since their convergence speed is exponential to the dimensionality of the solution domain, and existing methods estimate these 4DOF simultaneously. Our key idea is to speed up BnB-based methods by decoupling the joint pose into separate translation and rotation with the aid of known gravity directions. This effectively reduces the search domain to 3DOF+1DOF, thereby enhancing algorithm efficiency. Specifically, we propose a novel BnB-based consensus maximization method for a fast 3DOF translation search and derive the specific lower and upper bound functions. We then propose an efficient global voting method for estimating the rotation with 1DOF. To demonstrate the superiority of our proposed method, we conduct extensive experiments on both synthetic and real-world datasets. The experimental results show that (1) our proposed method is more robust against outliers and noise than several existing methods and far faster than the existing BnB-based 4DOF method by almost an order of magnitude, (2) our proposed method is robust against the biases in gravity directions, such that the general error of the IMU is acceptable, and (3) thanks to its significant robustness, our proposed method can solve the challenging problem of simultaneous pose and correspondence registration (SPCR). Moreover, the proposed approach is also more robust and accurate than several SPCR benchmark methods. Code is available at https://github.com/Xinyi-tum/Fast-and-Deterministic-Registration.

Semantic Communities from Graph-Inspired Visual Representations of Cityscapes

The swift development of autonomous vehicles raises the necessity of semantically mapping the environment by producing distinguishable representations to recognise similar areas. To this end, in this article, we present an efficient technique to cut up a robot’s trajectory into semantically consistent communities based on graph-inspired descriptors. This allows an agent to localise itself in future tasks under different environmental circumstances in an urban area. The proposed semantic grouping technique utilizes the Leiden Community Detection Algorithm (LeCDA), which is a novel and efficient method of low computational complexity and exploits semantic and topometric information from the observed scenes. The presented experimentation was carried out on a novel dataset from the city of Xanthi, Greece (dubbed as Gryphonurban urban dataset), which was recorded by RGB-D, IMU and GNSS sensors mounted on a moving vehicle. Our results exhibit the formulation of a semantic map with visually coherent communities and the realisation of an effective localisation mechanism for autonomous vehicles in urban environments.

Comparing the performance of lateral control algorithms on long rigid vehicles in urban environments

Existing research on autonomous vehicle control is largely focused on how control algorithms perform on cars; long vehicles or buses have not been significantly investigated. Existing studies indicate that people display positive attitudes to using autonomous buses, indicating the value of researching control algorithms for autonomous buses. To provide insight on the performance of control algorithms on autonomous buses, we compared multiple lateral control algorithms on how well they maneuver a long vehicle around three courses resembling urban environments. We compared the Stanley and pure pursuit control algorithms and two new control algorithms which were improved versions of the Stanley and pure pursuit controllers. We compared the control algorithms in a kinematic simulation which recorded the steering angle and cross track error from the front axle for each controller driving the vehicle at 50 km/h around the course. We hypothesized that the Stanley control algorithm would perform the best due to its success in navigating existing vehicles. The Stanley control algorithm had a low cross track error, but it had a large steering angle and large changes in steering angle. The pure pursuit controller had smoother changes in steering angle, but the cross track error was larger than the Stanley controller. Our results suggest that none of the algorithms we tested were optimal, and that an algorithm which can utilize the ability of the Stanley controller to maintain a low cross track error while also keeping a low steering angle change will perform the best for long vehicles.

УПРАВЛЕНЧЕСКИЕ РЕШЕНИЯ ДЛЯ РАЗВИТИЯ БЕСПИЛОТНОЙ АВИАЦИИ РОССИИ

This scientific article examines the use of unmanned aerial vehicles in urban life and education. In recent years, the development of unmanned aerial vehicles has achieved significant success, which has led to a growing interest in their use in various fields. In this study, we focus on the potential of unmanned aerial vehicles in urban environments and educational institutions. The main benefits are analyzed, such as time savings, reduced costs, and improved security.

A scalable time-division-based emergency messages broadcast scheme for connected and autonomous vehicles in urban environment

Connected and Autonomous Vehicles (CAVs) technology promises to revolutionize the transportation sector by solving many safety and traffic efficiency challenges while significantly enhancing passengers' and drivers' comfort. Most of these applications are built upon multi-hop broadcast of information among vehicles, leading to an increased contention and thus higher collision probability due to the limited bandwidth available for vehicular communications. In this paper, we propose a novel Time-Division-based Broadcast (TDB) scheme to mitigate interferences originating from hidden nodes (i.e., vehicles) to meet the stringent safety applications requirements. Then, we use this broadcast scheme as a basis for designing a new efficient and scalable protocol for emergency messages dissemination in urban vehicular networks (UV-TDB: an Urban, Vehicular and Time-Division-based Broadcast protocol). The simulation experiments show that UV-TDB significantly improves the performance of multi-hop broadcast and outperforms two state of the art protocols in terms of the achieved reduction in broadcast overhead (up to 77.9%), packet collisions ratio (up to 93.5%), interference rate (up to 43.5%) and increase in message reception rate (up to 337.7%). The results highlight also that UV-TDB adapts better to varying levels of vehicle density, making it more scalable.

Performance Evaluation of GNSS Position Augmentation Methods for Autonomous Vehicles in Urban Environments.

Global Navigation Satellite Systems provide autonomous vehicles with precise position information through the process of position augmentation. This paper presents a series of performance tests aimed to compare the position accuracy of augmentation techniques such as classical Differential Global Navigation Satellite System, Real-time Kinematic and Real-time eXtended. The aim is to understand the limitations and choose the best position augmentation technique in order to obtain accurate, trustworthy position estimates of a vehicle in urban environments. The tests are performed in and around the German cities of Wuppertal and Duesseldorf, using a vehicle fitted with the navigation system POS-LV 220, developed by Applanix Corporation. In order to evaluate the real-time performance of position augmentation techniques in a highly challenging environment, a total of four test regions are selected. The four test regions are characterized mainly by uneven terrain with tall buildings around the University of Wuppertal, flat terrain with roads of varying width in the city centre of Wuppertal and Duesseldorf and flat terrain in a tunnel section located in the city of Wuppertal. The performances of the different position augmentation are compared using a Root Mean Square (RMS) error estimate obtained as an output from the Applanix system. Furthermore, a High-Definition map of the environment is used for the purpose of model validation, which justifies the use of RMS error estimate as an evaluation metric for the performance analysis tests. According to the performance tests carried out as per the conditions specified in this paper, the Real-time eXtended (RTX) position augmentation method enables to obtain a more robust position information of the vehicle than Real-time Kinematic (RTK) method, with a typical accuracy of a few centimeter in an urban environment.

Decision-Making Method of Autonomous Vehicles in Urban Environments Considering Traffic Laws

In order to improve the efficiency and safety of autonomous vehicles’ decision-making process in complex urban scenarios, a decision-making method for hierarchical processing of static traffic law information and dynamic traffic participant information is proposed in this paper. In the decision-making method, the candidate behavior set is constructed by extracting the element of traffic laws and fully considering the traffic laws constraints to ensure the legality and effectiveness of the decision-making algorithm. Besides, four evaluation indicators and two-level entry threshold are designed to select the optimal driving behavior with the consideration of driving efficiency, ride safety and macro path requirement. The advantage of proposed method is that it avoids the problem of poor adaptability to traffic laws and regulations as the existing methods usually make decisions under the condition of mixed traffic laws and traffic participant information. A complete driving task simulation and analysis, including six typical urban traffic scenarios, is given under Matlab environment. The results show that the proposed decision-making method is able to make reasonable and feasible decisions and highly consistent with the actual driver’s decision-making behavior in complex urban scenarios, which verifies the effectiveness of the proposed method.

Choosing a Mode in Bangkok: Room for Shared Mobility?

Individual motorized vehicles in urban environments are inefficiently oversupplied both from the perspective of transport system efficiency and from the perspective of local and global environmental externalities. Shared mobility offers the promise of more efficient use of four-wheeler vehicles, while maintaining flexible routing. Here, we aim to understand the travel mode choices of commuters in Bangkok and explore the potential demand for shared mobility through examining both revealed and stated choices, based on our survey (n = 1239) and a systematic comparison of mode choice situations. Our multinomial logistic regression analysis indicates that commuters value time in their vehicles and accept fuel costs, but that they dislike wasting time walking, waiting, and searching for parking or pay for road use and parking. Our model results imply that shared taxi has a higher chance of being used as a door-to-door mode rather than as a competitor to motorcycle taxis as a feeder to the metro stations. Ride sharing gains substantial potential when private motorized cars are charged with the social external costs they cause via congestion charges and parking fees. Replacing cars with shared taxis as the daily choice for those living in detached houses will result in a 24–36% reduction of car trips on Bangkok roads.

Recent Advances in Sensor Integrity Monitoring Methods—A Review

In face of the high complexity of modern systems and the increasing reliance on technology, the strategies and methods to evaluate the integrity of systems and the information they provide are of great importance. The use of sensors to provide important information in various applications has become ubiquitous, which puts more pressure on the accuracy of sensor measurements. In the navigation field, in particular, the integrity of positioning information provided by sensors can be critical, as failures can lead to fatalities and catastrophic damages. Extensive research has been done into methods to assess and improve the integrity of positioning information provided by GNSS receivers, led by the high safety requirements in the aviation field. More recently, a shift into the research of integrity solutions suitable for the challenging positioning requirements of autonomous vehicles in urban environment has been a trend. There have been extensive advancements in developing integrity monitoring algorithms for GNSS and navigation in general. However, these methods have not been sufficiently extended into other types of sensors, and the development of integrity methods outside the navigation field have been scarce and scattered. This work aims to provide an overview of recent advances in the integrity monitoring field, including research outside the navigation domain. The goal is to give an introduction to the integrity monitoring concept to a broader audience, as these techniques have been highly specialized by GNSS experts and navigation related research, fostering multi-disciplinary approaches and creative use of existing methods in different areas and applications.

Electric vehicle charging stations emplacement using genetic algorithms and agent-based simulation

The increasingly evident incorporation of the electric vehicle in urban environments is an already undeniable change. Electric vehicles are appearing on the market with more autonomy and lower prices, which is facilitating the progressive change of the vehicle fleet. However, the electric vehicle brings with it the need to provide enough charging stations distributed throughout the city, so that the autonomy of the vehicle is not a problem. This work presents how a genetic algorithm that analyzes the open data sources of a city is used to propose the most suitable locations for these stations. This proposal is the input for a series of experiments that simulate the impact that has the placement of these stations along the city, in order to measure the benefits of the solution proposed by the genetic algorithm. To do this, an agent-based simulation infrastructure was built around a fleet simulator.

A model predictive control approach for highly automated vehicles in urban environments

AbstractIn this paper, a model predictive control (MPC) approach for the lateral and longitudinal control of a highly automated electric vehicle with all-wheel drive and dual-axis steering is presented. For the prediction of state trajectories a two-track vehicle model is used. The MPC problem for trajectory tracking is formulated by controlling the front and rear steering angle as well as the individual drive torques with respect to actuator and design constraints. Beside the steering angles, the MPC controller computes the individual drive torques to not only match the reference velocity but also to support the lateral dynamics of the vehicle using torque vectoring. The MPC problem is solved using ACADOS, a software package for efficiently solving optimal control problems. The effectiveness of the proposed MPC scheme is demonstrated via simulation.

VTGNet: A Vision-Based Trajectory Generation Network for Autonomous Vehicles in Urban Environments

Traditional methods for autonomous driving are implemented with many building blocks from perception, planning and control, making them difficult to generalize to varied scenarios due to complex assumptions and interdependencies. Recently, the end-to-end driving method has emerged, which performs well and generalizes to new environments by directly learning from export-provided data. However, many existing methods on this topic neglect to check the confidence of the driving actions and the ability to recover from driving mistakes. In this paper, we develop an uncertainty-aware end-to-end trajectory generation method based on imitation learning. It can extract spatiotemporal features from the front-view camera images for scene understanding, and then generate collision-free trajectories several seconds into the future. The experimental results suggest that under various weather and lighting conditions, our network can reliably generate trajectories in different urban environments, such as turning at intersections and slowing down for collision avoidance. Furthermore, closed-loop driving tests suggest that the proposed method achieves better cross-scene/platform driving results than the state-of-the-art (SOTA) end-to-end control method, where our model can recover from off-center and off-orientation errors and capture 80% of dangerous cases with high uncertainty estimations.