Search Results (17)

The research objective of this paper is to analyse what is behind the self-driving offer implemented in Phoenix (Arizona) by Waymo and a normal rent-a-car company by modelling both in e³value. A gap analysis proposes a new model of the rent-a-car business with the integration of a shared autonomous vehicle ride-hailing service. The goal is to encourage the growth of additional global shared autonomous vehicle trials and their incorporation into conventional businesses. The primary objective is to enhance shared autonomous mobility options, resulting in increased road safety, decreased traffic, and decreased emissions in urban areas. As a result, modelling Waymo can serve as a foundation for expanding the use of shared autonomous vehicles by other businesses in different geographic areas. Full article

The predictors of urban trip mode choice and one of its important components, public transit ridership, have still not been thoroughly investigated using case studies in Central Europe. Therefore, this study attempts to clarify the correlates of mode choices for commute travel and shopping, and entertainment travel to distant places, as well as the frequencies of public transit use of university students, using a wide range of explanatory variables covering individual, household, and socio-economic attributes as well as their perceptions, mobility, and the nearby built environment. The correlation hypothesis of these factors, especially the role of the street network, was tested by collecting the data from 1288 university students in Krakow and developing Binary Logistic and Ordinal Probit models. The results show that gender, age, car ownership, main daily activity, possession of a driving license, gross monthly income, duration of living in the current home, daily shopping area, sense of belonging to the neighborhood, quality of social/recreational facilities of the neighborhood, and commuting distance can predict commute and non-commute mode choices, while gender, daily activity, financial dependence from the family, entertainment place, quality of social/recreational facilities, residential self-selection, number of commute trips, time living in the current home, and street connectivity around home are significantly correlated with public transit use. Some of these findings are somewhat different from those regarding university students in Western Europe or other high-income countries. These results can be used for policy making to reduce students’ personal and household car use and increase sustainable modal share in Poland and similar neighboring countries. Full article

The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edge computing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of next-generation vehicular networks. Full article

Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify the accident locations; it only classifies whether a scene is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by (1) replacing the classification network with a detection one, (2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles’ bounding boxes while keeping the same training, validation, and testing samples, and (3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. The SDC-Net++ system is proposed to (1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and (2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird’s eye views, and with single-task networks, crash avoidance only, and multitask networks. The multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. The SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677%, respectively. Full article

When it comes to climate change, automated vehicles (AV) are often presented as a key factor to reducing emissions related with the transport sector. While studies promise emissions savings of up to 80%, it is often overlooked how AVs will be introduced and which transportation mode changes will arise from their implementation. Therefore, this online survey examined usage intentions regarding private and shared AV types, and underlying attitudes and mobility needs of 136 current users of different main modes of transport. Two main results counteract the general assumption of ecological sustainability benefits of AVs: First, current car drivers prefer private over shared AV types, even though notable sustainability gains can only be expected from shared AVs. Second, current users of more sustainable modes of transport (walking, bike, public transport) would replace theses modes by AVs for substantial shares of their trips, which represents a behavioural rebound effect, since AVs cannot be more sustainable than walking or biking. Group-specific mobility needs and knowledge gaps regarding the sustainability of different AV types are identified as reasons for these results and as starting points for deriving necessary measures accompanying the introduction of AVs into society through motivating ecologically sustainable transportation mode changes. Full article

Recent analysis has shown deteriorating traffic conditions in urban areas, caused by an increase in the motorization rate, which has risen to 66.6 vehicles per 100 inhabitants. As a result of the pandemic, individuality has grown, hence private vehicles are becoming more prevalent whilst public transport and sharing are negatively affected. Therefore, European policies have encouraged and innovated more sustainable mobility. Thus, the developed project aims to achieve more efficient mobility and more sustainable environments, towards social and economic well-being. The proposed means of transport aims to appeal to an audience with a reduced ability to drive a car as intended. The IDeS methodology was applied to develop a self-driving, urban micro mobility vehicle, aimed to give enough room and equipment for people with moving disabilities. The innovation of the IDeS method is state-of-the-art and ought to satisfy current product needs, which leads to an innovative micromobility vehicle and portrays a design for a car that will help to close the gaps in urban mobility. These design processes, which are distinguished by the fusion of several industrial techniques, enabled the development of a plan that addresses current mobility issues for disabled people and opens to new mobility prospects. Full article

In order that fully self-driving vehicles can be realized, it is believed that systems where the driver shares control and authority with the intelligent vehicle offer the most effective solution. An understanding of driving intention is the key to building a collaborative autonomous driving system. In this study, the proposed method incorporates the spatiotemporal features of driver behavior and forward-facing traffic scenes through a feature extraction module; the joint representation was input into an inference module for obtaining driver intentions. The feature extraction module was a two-stream structure that was designed based on a deep three-dimensional convolutional neural network. To accommodate the differences in video data inside and outside the cab, the two-stream network consists of a slow pathway that processes the driver behavior data with low frame rates, along with a fast pathway that processes traffic scene data with high frame rates. Then, a gated recurrent unit, based on a recurrent neural network, and a fully connected layer constitute an intent inference module to estimate the driver’s lane-change and turning intentions. A public dataset, Brain4Cars, was used to validate the proposed method. The results showed that compared with modeling using the data related to driver behaviors, the ability of intention inference is significantly improved after integrating traffic scene information. The overall accuracy of the intention inference of five intents was 84.92% at a time of 1 s prior to the maneuver, indicating that making full use of traffic scene information was an effective way to improve inference performance. Full article

Environmental awareness and technological advancements for self-driving cars are close to making autonomous vehicles (AV) a reality in everyday scenarios and a part of smart cities’ transportation systems. The perception of safety and trust towards AVs of passengers and other agents in the urban scenario, being pedestrians, cyclists, scooter drivers or car drivers, is of primary importance and the theme of investigation of many research groups. Driver-to-driver communication channels as much as car-to-driver human–machine interfaces (HMI) are well established and part of normal training and experience. The situation is different when users must cope with driverless and autonomous vehicles, both as passengers and as agents sharing the same urban domain. This research focuses on the new challenges of connected driverless vehicles, investigating an emerging topic, namely the language of driving (LoD) between these machines and humans participating in traffic scenarios. This work presents the results of a field study conducted at Tallinn University Technology campus with the ISEAUTO autonomous driving shuttle, including interviews with 176 subjects communicating using LoD. Furthermore, this study combines expert focus group interviews to build a joint base of needs and requirements for AVs in public spaces. Based on previous studies and questionnaire results, we established the hypotheses that we can enhance physical survey results using experimental scenarios with VR/AR tools to allow the fast prototyping of different external and internal HMIs, facilitating the assessment of communication efficacy, evaluation of usability, and impact on the users. The aim is to point out how we can enhance AV design and LoD communications using XR tools. The scenarios were chosen to be inclusive and support the needs of different demographics while at the same time determining the limitations of surveys and real-world experimental scenarios in LoD testing and design for future pilots. Full article

Finding a sustainable mobility solution for the future is one of the most competitive challenges in the logistics and transportation sector nowadays. Researchers, universities, and companies are working intensively to provide novel mobility options that can be environmentally friendly and sustainable. While autonomous car-sharing services have been introduced as a very promising solution, an innovative alternative is arising using self-driving bikes. Shared autonomous cargo bike fleets are likely to increase the livability and sustainability of the city as the use of cargo bikes in an on-demand mobility service can replace the use of cars for short-distance trips and enhance connectivity to public transportation. However, more research is still needed to develop this new concept. To address this research gap, this paper examines the on-demand shared-use autonomous bikes service (OSABS) from a fleet management perspective. In fact, such a system requires good management strategies in order to ensure its efficiency. Through an agent-based simulation of a case study in Magdeburg, we investigate various parameters that can influence the performance and the service quality of OSABS such as the rebalancing frequency and the relocation type. Tests were performed for two different operational areas: the inner city and the complete city of Magdeburg. We conclude with different management insights for an optimized functioning of the system. Full article

Finding a sustainable mobility solution for the future is one of the most competitive challenges in the logistics and mobility sector at present. Policymakers, researchers, and companies are working intensively to provide novel options that are environmentally friendly and sustainable. While autonomous car-sharing services have been introduced as a very promising solution, an innovative alternative is arising: the use of self-driving bikes. Shared autonomous cargo-bike fleets are likely to increase the livability and sustainability of the city, as the use of cargo-bikes in an on-demand mobility service can replace the use of cars for short-distance trips and enhance connectivity to public transportation. However, more research is needed to develop this new concept. In this paper, we investigate different rebalancing strategies for an on-demand, shared-use, self-driving cargo-bikes service (OSABS). We simulate a case study of the system in the inner city of Magdeburg using AnyLogic. The simulation model allows us to evaluate the impact of rebalancing on service level, idle mileage, and energy consumption. We conclude that the best proactive rebalancing strategy for our case study is to relocate bikes only between neighboring regions. We also acknowledge the importance of bike relocation to improve service efficiency and reduce fleet size. Full article

In the United States, older adults (age 65 and older) rely on private automobiles for transportation. For those who stop driving, access to alternative modes of transportation is important for health, wellbeing, mobility, and independence. This paper explores older adult willingness to use fully autonomous vehicle (FAV) ride sharing and the features or services of FAV ride sharing that would make them willing to take a ride. These data were gathered as part of a larger qualitative research study designed to explore the factors affecting older adult use of ride share services. For the larger study, we conducted 68 telephone interviews with older adults, and 10 in-person focus groups with 56 older adults, including individuals who both used and never used ride share services. We used a convenience sample recruited by study partners, including ride share and transportation services and a recruitment firm. The predominant thematic findings of the qualitative analysis included a desire for a proven safety record in terms of performance and technology, followed by dependability and accuracy of FAV ride sharing. Older adults’ concerns about FAV ride sharing included safety concerns and preferences for social interaction with drivers. Ride share services that use FAVs in the future may need to tailor transportation offerings for older adults to increase their willingness to use FAVS to support their mobility and social needs. Full article

The advantage of using the Network Address Translation device is that the internal IP address, which makes the IP address space of Internet of Things (IoT) devices expanded, is invisible from the outside and safe from external attacks. However, the use of these private IPv4 addresses poses traversal problems, especially for the mobile IoTs to operate peer-to-peer applications. An alternative solution is to use IPv6 technologies for future IoT devices. However, IPv6 package, including IPSec, is too complex to apply to the IoT device because it is a technology developed for the user terminal with enough computing power. This paper proposes a gatekeeper to enable the real IP addresses of IoTs inside the same subnetwork to be not explicitly addressable and visible from outside of the gatekeeper. Each IoT device publishes its virtual IP address via the Registrar Server or Domain Name System (DNS) with which the gatekeeper shares the address mapping information. While the gatekeeper maintains the mapping information for the local IoT devices, the registration server or DNS has global address mapping information so that any peer can reach the mapping information. All incoming and outgoing packets must pass through the gatekeeper responsible for the address conversion and security checks for them from the entrance. This paper aims to apply our gatekeeper system to a platform of self-driving cars that allows surrounding IoT cameras and autonomous vehicles to communicate with each other securely, safely, and rapidly. So, this paper finally analyzes improvement effects on latency to show that our gatekeeper system guarantees the latency goal of 20 ms under the environment of 5G links. Full article

Vehicular fog computing is attractive for sharing computing resources and data for safety and infortainment of self-driving cars. Recently, the V2X communication technology using mm-Wave frequency spectrum accelerates such future mobile computing with large bandwidth and beam-forming using a directional antenna. Although the beam-forming technique requires a complicate procedure for beam alignment, it can reduce mutual interference by spatial diversity. From the beam-forming scheduling, the vehicular fog can improve network performance, which is limited by data locations. Beams toward a vehicle for the same content should be scheduled in the time domain. Instead, we propose to replicate the content to multiple vehicles nearby to diversify beam directions. However, it is a challenge for vehicles to cache the content because the content caching costs not only limited local storage, but data transmission for other vehicles. For this, we adopt evolutionary game theory in which vehicles learn an evolutionarily stable strategy (ESS) from repeated games and maximize social utility. In this paper, we contribute to modeling a road segmentation for the mm-Wave V2X communication in order to derive connectivity probability with distributed content caches for the vehicular fog, and centralized and distributed algorithms for the evolutionary content cache game. From experiments, we confirm that content cache can improve V2X connectivity and the proposed evolution algorithm leads vehicles to choose the ESS for the content cache in the vehicular fog. Full article

Nowadays, the concept of new mobility solutions like shared mobility systems is becoming more and more popular in current transport systems. The next technological step will be the idea of replacing traditional vehicles with autonomous ones. Because autonomous vehicles are a new concept in the automotive market, we dedicated this article to the idea of using autonomous vehicles as a part of car-sharing systems in intelligent, urban transport systems. The research herein is focused on the economic aspects of using autonomous vehicles in comparison to the classic car fleet available in car-sharing systems and to vehicles that belong to individual owners. We present our method for appropriate fleet selection based on the Delphi method and the calculations made through a scientific experiment performed based on Hartley’s plan. The results indicate the relation of travel parameters (including vehicle type) to the total cost of travel in urban transport systems. We also present the main terms related to autonomous vehicles. This article provides support for people who want to deepen knowledge about autonomous vehicles and new mobility solutions used in urban transport systems. Full article

Renewable, reliable, and affordable future power, heat, and transportation systems require efficient and versatile energy storage and distribution systems. If solar and wind electricity are the only renewable energy sources, what role can hydrogen and fuel cell electric vehicles (FCEVs) have in providing year-round 100% renewable, reliable, and affordable energy for power, heat, and transportation for smart urban areas in European climates? The designed system for smart urban areas uses hydrogen production and FCEVs through vehicle-to-grid (FCEV2G) for balancing electricity demand and supply. A techno-economic analysis was done for two technology development scenarios and two different European climates. Electricity and hydrogen supply is fully renewable and guaranteed at all times. Combining the output of thousands of grid-connected FCEVs results in large overcapacities being able to balance large deficits. Self-driving, connecting, and free-floating car-sharing fleets could facilitate vehicle scheduling. Extreme peaks in balancing never exceed more than 50% of the available FCEV2G capacity. A simple comparison shows that the cost of energy for an average household in the Mid Century scenario is affordable: 520–770 €/year (without taxes and levies), which is 65% less compared to the present fossil situation. The system levelized costs in the Mid Century scenario are 71–104 €/MWh for electricity and 2.6–3.0 €/kg for hydrogen—and we expect that further cost reductions are possible. Full article