Connected Vehicle Research Articles

Montaigne, Lukrecjusz i Nowy Świat

In this article, my aim is to interpret Michel de Montaigne’s essay On Coaches, firstly, as consistent, and secondly, as deeply marked by Lucretian ideas. I analyze three contexts in which the titular ‘coaches’ are present: luxury, war, and fear of death. Furthermore, I argue that they constitute mutually interacting ‘force fields’ that should be considered in relation to each other.Following this scheme, I reconstruct the relationship between this essay and De rerum natura on three levels: 1) a discussion involving the Lucretian idea of the finite and young world; 2) a specific repetition of Lucretius’ gesture: moral evaluation imposed on a basically neutral recognition of historical change and movement; 3) the connection of vehicles as symbols of luxury with the problem of the fear of death, or the lack of courage, as a source of moral degeneration.

Creating a space for the sacred in a secular world: Shabbat dinners as a vehicle for collective effervescence, social connection, and meaning

Scholars have long argued that religious rituals are essential for maintaining society, bringing meaning to life and increasing connections via the collective effervescence that they engender. A world in which fewer people attend religious services does not mean that people no longer benefit from the important outcomes associated with religious rituals. The current research examined whether people experience collective effervescence and positive outcomes in home-based rituals—specifically, Shabbat dinners facilitated by a Jewish organization that provides resources for home-based Shabbat dinners. Survey data was collected on almost 1000 attendees of the Shabbat dinners. The results suggest that (a) Shabbat dinners include both components of collective effervescence (a sensation of sacredness and connection to others); (b) “transcendence” collective effervescence was related to religiosity and meaningfulness, whereas “connectedness”collective effervescence was related to belonging and satisfaction; and (c) using instructional resources predicted increased meaning via religiosity and increased satisfaction via belonging. The authors conclude that home-based rituals can provide valuable outcomes in a world in which people are less likely to attend religious services.

Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment

With the gradual development of autonomous driving and connected communication technology, urban road networks will be shared by a combination of Human-driven vehicles (HDVs) and Connected vehicles (CVs) for a long era. Besides, as the actual vehicle operator, the driver regulates the vehicle state with an accelerator, gear lever, or brake, whereas the stepwise acceleration process is overlooked in the literature. Motivated by this, comprehensive kinetic difference between the two types of vehicles, we propose a novel heterogeneous multi-phase traffic flow accounting for the HDVs and CVs to close this hole. In the section on linear stability analysis, the reductive perturbation approach was applied to figure out the stability norm of the new model. The findings show that, on the one hand, inhibiting traffic bottlenecks is positively impacted by the penetration rate of CVs and the number of preceding vehicles taken into account; on the other hand, by varying the headway between following vehicles, the multiple-phase transition occurs; consequently, the number of turning points in the optimal velocity function is the sole factor that affects the number of stages in multi-phase transitions. Subsequently, the modified Korteweg-de Vries (mKdV) equation corresponding to the new model is deduced to investigate the nonlinear phenomenon of traffic flow at the vicinity of the critical point, and the kink-antikink soliton wave solution by solving the above characteristic equation can describe the “stop-and-go” phenomenon in real traffic flow. Finally, the conclusion of the numerical experiments is consistent with the above theoretical analysis. This research can provide insight into the dynamic evolution of road traffic flow during the transition from HDVs to semi-CVs and eventually to CVs.

Design of a Modularization-Based Automation Performance Simulation Framework for Multi-Vehicle Interaction System

With the electrification and connectivity of vehicles in transportation, traditional vehicles with single drivetrains are being replaced by pure electric or hybrid electric vehicles (HEVs). This evolution has given rise to diverse electromechanical coupling drivetrains. There is a pressing need to update simulation software to assess the economic performance of vehicles in various environments, and promote sustainable development and energy conservation. This paper presents a unified framework for the construction and automated operation of large-scale automated vehicle simulations with multiple drivetrain types, facilitating synchronous information exchange among vehicles. Central to the framework is the development of a plug-and-play vehicle model based on a modular component design, facilitating the rapid assembly of vehicles with varied drivetrain configurations and standardizing simulation file management. Additionally, a standardized simulation process construction is established to accommodate the automated operation of simulations. Furthermore, a data scheduling method among vehicles is introduced to achieve multi-vehicle interconnection simulation. Finally, the effectiveness of the framework is demonstrated through a case study involving queue-following control for HEVs. This framework aims to provide a comprehensive solution for quickly establishing automated simulation environments for multi-vehicle interaction, enhancing model reusability and scalability.

Evaluating the Impact of V2V Warning Information on Driving Behavior Modification Using Empirical Connected Vehicle Data

A connected vehicle (CV) enables vehicles to communicate not only with other vehicles but also the road infrastructure based on wireless communication technologies. A road system with CVs, which is often referred to as a cooperative intelligent transportation system (C-ITS), provides drivers with road and traffic condition information using an in-vehicle warning system. Road environments with CVs induce drivers to reduce their speed while increasing the spacing or changing lanes to avoid potential risks downstream. Such avoidance maneuvers can be considered to improve driving behavior from a traffic safety point of view. This study seeks to quantitatively evaluate the effect of in-vehicle warning information using per-vehicle data (PVD) collected from freeway C-ITSs. The PVD are reproduced to extract the speed–spacing relationship and are evaluated to determine whether the warning information induces drivers to drive in a conservative way. This study reveals that the in-vehicle warning prompts drivers to increase the spacing while decreasing their speed in the majority of samples. The rate of conservative driving behavior tends to increase during the initial operation period, but no significant changes were observed after this period; that is, the reliability of in-vehicle warning information is not constant in the CV environment.

Disruptive Innovation for Automotive Industry and the Solutions for Enterprise Innovation System Reengineering

Review Disruptive Innovation for Automotive Industry and the Solutions for Enterprise Innovation System Reengineering Weiqun Ren 1 1 Dongfeng Commercial Vehicles Co. Ltd., Wuhan 430000, China * Correspondence: renweiqun@tsinghua.org.cn Received: 26 January 2024 Accepted: 6 March 2024 Published: 11 March 2024 Abstract: Focus on the current disruptive innovation trends, automotive industry needs to have solutions for enterprise innovation system reengineering. The innovations include the energy and power technology bringing vehicle powertrain electrification, intelligent control technology bringing autonomous drive/intelligent vehicle, and communication technology bringing vehicle connectivity and sharing. Facing the impact of these, enterprise innovation system reengineering needs to be considering the effects to strategic planning, product development process, and new business models, etc.

Migrant Agricultural Workers’ Experiences of Support in Three Migrant‐Intensive Communities in Canada

Canada has intensified its reliance on temporary foreign workers, including migrant agricultural workers (MAWs) who have contributed to its agriculture sector, rural economies, and food security for decades. These workers live and work in rural communities across Canada for up to two years. Thousands of MAWs engage in recurring cyclical migration, often returning to the same rural communities in Canada for decades, while others are undocumented. Yet MAWs do not have access to the supports and services provided for immigrant newcomers and pathways for permanent residence. The exclusion of these workers from such entitlements, including labour mobility, reinforces their precarity, inhibits their sense of belonging, and reflects the stratification built into Canada’s migration regime. This article draws on interviews with 98 MAWs in three migrant‐intensive regions in southwestern Ontario to examine how workers construct and describe support in relation to co‐workers, employers, residents, and community organizations. Drawing on conceptualizations of support as an important vehicle for social connection and inclusion that comprises social and citizenship belonging, we document how the strategies MAWs employ to forge connections are enabled or undermined by Canada’s Temporary Foreign Worker Program, community dynamics, and the broader forces of racialization, gender, and exclusion. This article contributes to the limited scholarship on the support landscape for MAWs, whose experiences foreground the contested nature of belonging and inclusion among migrant populations across smaller cities and rural areas.

Simulation of Reducing Pollutants and Fuel Consumption Through the Connection of Commercial Vehicles with Infrastructure

Simulation of Reducing Pollutants and Fuel Consumption Through the Connection of Commercial Vehicles with Infrastructure

Comparison at Scale of Traffic Signal Cycle Split Failure Identification from High-Resolution Controller and Connected Vehicle Trajectory Data

Split failures have been a conventional method to estimate overcapacity at signalized intersections. Currently, split failures are estimated from high-resolution (HR) traffic signal controller event data by evaluating occupancy at the stop bar. Recently, a technique that uses high-fidelity connected vehicle (CV) trajectory data to estimate split failures has been developed and has been adopted by some agencies. This paper compares cycle-by-cycle split failure estimations from both techniques for 42 signalized intersections across central Indiana. CV trajectories were assigned to a cycle based on their arrival characteristics. Then, HR and CV data were used to determine whether each cycle split fails. Finally, agreements and discrepancies were quantified and evaluated. The results obtained after analyzing over 35,000 cycles showed that both techniques produce similar overall split failure estimations. The HR and the CV methods identified 4% and 3% of all cycles as split failing, respectively. However, only 23% of all cycles determined as split failing with the HR approach were also identified as split failing with CV data. Similarly, only 30% of all cycles determined as split failing with the CV approach were also identified as split failing with the HR approach. This indicates significant discrepancies on a cycle-by-cycle basis. Using CV data to identify split failing cycles produces more conservative results and is based on the entire experience of traversing vehicles. If data are available, the authors recommend the CV approach when allocating limited agency resources for operational improvement activities.

Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor

In this study, we developed a real-time connected vehicle (CV) speed advisory application that uses public cloud services and tested it on a simulated signalized corridor for different roadway traffic conditions. First, we developed a scalable serverless cloud computing architecture leveraging public cloud services offered by Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we developed an optimization-based real-time CV speed advisory algorithm by taking a modular design approach, which makes the application automatically scalable and deployable in the cloud using the serverless architecture. Third, we developed a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called Simulation of Urban Mobility (SUMO). Our analyses based on different roadway traffic conditions showed that the serverless CV speed advisory application meets the latency requirement of real-time CV mobility applications. Besides, our serverless CV speed advisory application reduced the average stopped delay (by 77%) and the aggregated risk of collision (by 21%) at the signalized intersections of a corridor. These prove the feasibility as well as the efficacy of utilizing public cloud infrastructure to implement real-time roadway traffic management applications in a CV environment.

Transit Signal Priority under Connected Vehicle Environment: Deep Reinforcement Learning Approach

Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service and enhance transportation equity. Conventional TSP strategies often ignore the fluctuation of passenger occupancy in transit vehicles, leading to sub-optimal solutions for the entire system. The use of Connected Vehicle (CV) technology enables the adoption of a more fine-grained objective in optimizing traffic signals, such as person delay, by allowing real-time information on passenger occupancy to be obtained. In this study, a deep reinforcement learning algorithm, deep Q-network (DQN), is applied to develop a traffic signal controller that minimizes the average person delay. The proposed DQN controller is tested in a simulation environment modeled after a real-world intersection and compared with pretimed and actuated controllers. Results show that the proposed DQN controller has the best performance in terms of average person delay. Compared to the baseline, it reduces the average person delay by 18.77% in peak hours and 23.37% in off-peak hours. Furthermore, it also results in decreased average delays for both buses and cars. The sensitivity analysis results indicate that the proposed controller has the potential for practical applications, as it can effectively handle some dynamic changes.

Modeling the influence of connected vehicles on driving behaviors and safety outcomes in highway crash scenarios across varied weather conditions: A multigroup structural equation modeling analysis using a driving simulator experiment

Equipped with advanced sensors and capable of relaying safety messages to drivers, connected vehicles (CVs) hold the potential to reduce crashes. The goal of this study is to assess the impacts of CV technologies on driving behaviors and safety outcomes in highway crash scenarios under diverse weather conditions, including clear and foggy weather. A driving simulator experiment was conducted and the multigroup structural equation modeling (SEM) was employed to explore the complex interrelationships between the propensity of traffic conflicts, utilization of CV alerts, weather, psychological factors, driving behaviors, and other relevant variables for two different crash locations, namely a straight section and a horizontal curve. Two latent psychological factors including aggressiveness and unawareness were constructed from driving behavior as vehicles passed by crash scenes such as brake, throttle, steering angle, lane offset, and yaw. The SEM can measure latent psychological factors and model interrelationships concurrently through a single statistical estimation procedure. Results of the multigroup SEM showed that CV alerts could significantly reduce the unawareness on a horizontal curve and thus lower the propensity of traffic conflicts. Additionally, the overall effect of foggy weather on conflicts was found to be positive on a horizontal curve, despite the potential benefit of improving situational awareness. In contrast, the single group SEM failed to reveal any significant interrelationships in its structural model by pooling data from both crash locations. The obtained insights can guide the development of driving assistance systems, highlighting the necessity of customization considering weather conditions and location-specific factors.

Evaluating the Efficacy of Real-Time Connected Vehicle Basic Safety Messages in Mitigating Aberrant Driving Behaviour and Risk of Vehicle Crashes: Preliminary Insights from Highway Scenarios

Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including car-following, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management.

Efficient content caching for 5G assisted vehicular networks

Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale–Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time.

Applications of using connected vehicle data for pavement quality analysis

Current quantitative methods to evaluate pavement conditions in the United States are most commonly focused on construction acceptance using the International Roughness Index (IRI). However, from an asset management perspective, qualitative visual inspection techniques are the most prevalent. Modern vehicles with factory-equipped sensors drive these roadways daily and can passively assess the condition of infrastructure at an accuracy level somewhere between qualitative assessment and rigorous construction acceptance techniques. This paper compares crowdsourced ride quality data with an industry standard inertial profiler on a 7-mile bi-directional construction zone. A linear correlation was performed on 14 miles of I-65 that resulted in an R2 of 0.7 and a p-value of <0.001, but with a modest fixed offset bias. The scalability of these techniques is illustrated with graphics characterizing IRI values obtained from 730,000 crowdsourced data segments over 5,800 miles of I-80 in April of 2022 and October 2022. This paper looks at the use of standard original equipment manufacturer (OEM) on-board sensor data from production vehicles to assess approximately 100 miles of roadway pavements before, during, and after construction. The completed construction projects observed IRI improvements of 10 in/mi to 100 in/mi. These results suggest that it is now possible to monitor pavement ride quality at a system level, even with a small proportion of connected vehicles (CV) providing roughness data.

Cybersecurity of Hybrid Electric City Bus With V2C Connectivity

Leveraging cloud computing resources and previews in intelligent transportation systems to improve electric city buses (ECBs) energy system performance is an emerging topic. However, internet connectivity will expose the ECB to cyberattacks, which will cause service interruption and downstream financial implications. In this study, detection and protection systems are designed under a proposed architecture for ECBs with Vehicle-to-Cloud connectivity to address the denial-of-service (DDoS) attack. The analysis conducted in this paper shows that the distributed DDoS attack is most crucial because it is easy to be launched and hard to be defended. By overwhelming traffics with spam, DDoS attacks can easily cut off the vehicle connection to cloud servers and disable all (compromised and surviving) ECBs that rely on vehicle-to-cloud connectivity. The simulation results indicate that, even though 99.9% of ECBs in the system are compromised and converted to attacking sources, with the proposed security systems in the cloud, the blocking probability between servers and surviving filed devices is kept below 28.08%. Besides, for a surviving field device protected by the proposed approach, harsh DDoS attacks only degrade the energy efficiency and battery life performance of the tested energy management system by 0.2288% when compared to the benchmark results.

Navigation Route Planning for Tourism Intelligent Connected Vehicle Based on the Symmetrical Spatial Clustering and Improved Fruit Fly Optimization Algorithm

The intelligent connected vehicle (ICV) decision-making system needs to match tourist interests and search for the route with the lowest travel cost when recommending POIs (Points of Interest) and navigation tour routes. In response to this research objective, we construct a navigation route-planning model for tourism intelligent connected vehicles based on symmetrical spatial clustering and improved fruit fly optimization algorithm. Firstly, we construct the POI feature attribute clustering algorithm based on the spatial decision forest to achieve the optimal POI recommendation. Secondly, we construct the POI spatial attribute clustering algorithm based on the SA-AGNES (Spatial Accessibility-Agglomerative Nesting) to achieve the spatial modeling between POIs and ICV clusters. On the basis of POI feature attribute and spatial attribute, we construct the POI recommendation algorithm for the ICV navigation routes based on the attribute weights. On the basis of the recommended POIs, we construct the tourism ICV navigation route-planning model based on the improved fruit fly optimization algorithm. Experiments prove that the proposed algorithm can accurately output POIs that match tourists’ interests and needs, and find out the ICV navigation route with the lowest travel cost. Compared with the commonly used map route-planning methods and traditional route-searching algorithms, the proposed algorithm can reduce the travel costs by 15.22% at most, which can also effectively reduce the energy consumption of the ICV system, and improve the efficiency of sight-seeing and traveling for tourists.

Vehicle trajectory reconstruction for freeway traffic considering lane changing behaviors

Vehicle trajectory data provides critical information for many transportation applications. Due to limitations in the data collection techniques, usually, only partial trajectories can be obtained. As a result, trajectory reconstruction where the missing trajectories are inferenced by the observed data is an essential step for many downstream applications. Existing studies usually consider a connected vehicle (CV) environment for trajectory data collection and ignore the lane-changing (LC) behaviors in the reconstruction process. The deployment of connected and autonomous vehicles (CAVs) makes it possible to collect trajectory data more efficiently with much lower penetrations. This study proposes a vehicle trajectory reconstruction algorithm considering LC maneuvers in the CAV environment. The Pettit test and a rule-based optimization algorithm are designed to predict the possible LC time points. Then two car-following models are applied to reconstruct trajectories. The NGSIM US101 dataset is applied to evaluate the proposed reconstruction algorithm under varying CAV penetration rates (PRs) (e.g., 2%, 3%, 5%). The prediction of LC time points achieves high accuracy with average prediction errors less than 1 s under CAV PRs greater than 2%. Compared to the ground truth trajectories, the reconstructed trajectories have the mean absolute error (MAE) less than one vehicle length under 3% and higher CAV PRs.

A Systematic Review on Security Attacks and Countermeasures in Automotive Ethernet

In the past years, the automotive industry has experienced a technological revolution driven by the increasing demand of connectivity and data to develop driver-assistance systems and autonomous vehicles, and improve the mobility experience. To provide higher bandwidth in in-vehicle communication networks, carmakers are choosing Ethernet technology, which becomes Automotive Ethernet (AE) when applied in in-vehicle communication networks. However, with the rise of vehicle connectivity, the cybersecurity of vehicle systems has become a primary concern for the automotive industry. To address this issue, we conducted a systematic review, deeply analyzing the impact of AE on security and safety, and comparing it with the current in-vehicle communication solutions like Controller Area Network protocol. We retrieved the key security attacks and mitigations proposed in the current literature to highlight their significance, including a mapping between the regulation UNECE WP.29 R155 and the retrieved answers. We found that the industry has only implemented some automotive-dedicated Ethernet solutions to date. In the near future, the vehicle and road ecosystems may require more exclusive automotive solutions to meet specific constraints such as low latency. Our results can provide a comprehensive baseline, both for industry and academia, for the current and future development of AE.

Integrated torque-vectoring and anti-roll moment distribution strategies based on optimal control: influence of model complexity and road curvature preview

Although the vehicle dynamics effects of variable anti-roll moment distribution actuated through active suspension systems are widely discussed in the literature, their model-based control has only been recently analysed, given the highly nonlinear nature of the involved dynamics. Moreover, the available studies do not discuss the trade-off between internal model complexity and controller performance, nor analyse the opportunities offered by vehicle connectivity, which enables the prediction of the steering angle and reference yaw rate profiles ahead. To address the gap, this paper introduces and assesses three optimal controllers for an electric vehicle with active suspensions, multiple powertrains, and a brake-by-wire system. The formulations are: (a) a gain scheduled output feedback linear quadratic regulator (OFLQR); (b) a nonlinear model predictive controller using a three-degree-of-freedom prediction model, without and with preview of the steering angle and reference yaw rate ahead, respectively referred to as NMPC-3 and NMPC-3-Pre; and (c) a nonlinear model predictive controller based on an eight-degree-of-freedom prediction model, referred to as NMPC-8 and NMPC-8-Pre depending on the absence or presence of preview. The results on an experimentally validated model show that: (i) NMPC-8 provides evident yaw rate tracking benefits with respect to (w.r.t) OFLQR and NMPC-3; and (ii) NMPC-8-Pre can bring ∼20% yaw rate tracking improvement w.r.t. an optimally tuned NMPC-8 configuration.