No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Automated vehicles as the future of road transport
Abstract
The aim of the topic on the future of automated vehicles in road transport was to analyze innovative solutions in a sector that is very important for society, which is undoubtedly transport. A very big challenge for innovation in the transport sector is the automation of means of transport, from all its sectors (road, sea, inland, rail and air transport). The scope of this study includes the analysis of literature in terms of research and technological development of automated vehicles and related innovative solutions for passenger and freight transport. The novelty presented in the study constituting a huge potential in the development of automated transport is C-V2X (a vehicle connected to everything in direct communication mode) as a future communication system of traffic participants, which includes other more specific types of communication technologies such as: V2V, V2I, V2P and V2N. Attention was also paid to the proper terminology of automated vehicles often called autonomous. Autonomy refers to independence, and the term "autonomous" is misleading in relation to automated driving technology. Even the most advanced automated driving systems are not "stand-alone" because they work on the basis of algorithms, and thus they follow the user's instructions in a different way. The classification, which is most often used to describe the degree of vehicle automation as defined in the standards of the International Society of Automotive Engineers (SAE), has also been presented and characterized.
ResearchGate has not been able to resolve any citations for this publication.
A perfect storm of new technologies and new business models is transforming not only our vehicles, but everything about how we get around, and how we live our lives.
The JRC report “The future of road transport - Implications of automated, connected, low-carbon and shared mobility” looks at some main enablers of the transformation of road transport, such as data governance, infrastructures, communication technologies and cybersecurity, and legislation. It discusses the potential impacts on the economy, employment and skills, energy use and emissions, the sustainability of raw materials, democracy, privacy and social fairness, as well as on the urban context.
It shows how the massive changes on the horizon represent an opportunity to move towards a transport system that is more efficient, safer, less polluting and more accessible to larger parts of society than the current one centred on car ownership. However, new transport technologies, on their own, won't spontaneously make our lives better without upgrading our transport systems and policies to the 21st century. The improvement of governance and the development of innovative mobility solutions will be crucial to ensure that the future of transport is cleaner and more equitable than its car-centred present.
Nowadays, the dynamics of variability marked by technologisation and digitisation cannot be underestimated. New technologies identified with Economy 4.0 affect almost every dimension of the modern world. The level of competitiveness of modern economies is determined by the implementation and diffusion of innovations based on new technologies. Artificial Intelligence, the Internet of Things (IoT), the Internet of Everything (IoE), hyperconnectivity, cloud computing applications and services, Big Data Analytics (BDA), Big-Dataas-a-Service (BDaaS), automation and robotisation are just a few of the technologies that the authors found worth looking at in more detail with regard to transport. Digital transformation is also a new opportunity as well as a challenge for business. This paper indicates the metamorphosis that transport has undergone as a result of the injection of new technologies. The presented material has been collected in the course of the source research carried out using the idiographic method. A critical analysis of available documents and literature, as well as digital sources, has made it possible to identify the benefits of injecting new technologies in transport as an effect of adaptability to the digital age.
Reflecting the increasing demand for harmonization of human machine interfaces (HMI) of automated vehicles, different taxonomies of use cases for investigating automated driving systems (ADS) have been proposed. Existing taxonomies tend to serve specific purposes such as categorizing transitions between automation modes; however, they cannot be generalized to different systems or combinations of systems. In particular, there is no exhaustive set of use cases that allows entities to assess and validate the HMI of a given ADS that takes into account all possible system modes and transitions. The present paper describes a newly developed framework based on combinatorics of SAE (Society of Automotive Engineers) automation levels that incorporates a comprehensive taxonomy of use cases required for the assessment and validation of ADS HMIs. This forms a much-needed basis for test methods required to verify whether an HMI meets minimum requirements such as those outlined in the National Highway Traffic Safety Administration’s Federal Automated Vehicles policy.
The driving task is becoming increasingly automated, thus changing the driver's role. Moreover, in-vehicle information systems using different display positions and information processing channels might encourage secondary task engagement. During manual driving scenarios, varying secondary tasks and display positions could influence driver's glance behavior. However, their impact on the driver's capability to monitor the partially automated driving systems has not yet been determined. The current study assessed both the effects of different secondary tasks (Surrogate Reference Task (SuRT) vs. text reading) and display positions (head-up display (HUD) vs. center console) on driver's glance behavior during partially automated driving. Participants engaged in several secondary tasks that were presented on different display positions while monitoring the partially automated system during a simulated car following task. Different automation system failures regarding the lateral and longitudinal control occurred while driving. A head-mounted eye-tracker recorded the participants' glance behavior. Repeated measures ANOVAs revealed that the HUD yielded considerably longer eyes-on display time (total and mean glance durations) than the center console. Moreover, the text reading task resulted in longer total and mean glance durations than the SuRT. Similar to manual driving scenarios, the results showed a consistent effect of display position and secondary task on the driver's glance behavior. Despite the longer eyes-on display time for the HUD, its proximity to the driving environment might enable a faster identification of and reaction to critical situations (e.g., due to system failures).
Cdo vit, 1.2 milionë njerëz humbasin jetën nga aksidentet automobilistike, dhe mbi më shumë se 50 milionë lëndohen. Shumica e aksidenteve, ndodhin për shkak të gabimeve të drejtuesve të automjeteve dhe shkaqeve të tjera të parandalueshme. Megjithëse numri I përgjithshëm I viktimave të trafikut është tragjikisht I madh, shkalla e dështimit të shoferit njerëzor në gjëndje të ndërgjegjshme është shumë e vogel. Sipas statistikave shoferi njeri rezulton të bëjë një gabim fatal një herë në 88 milion milje.
Ndërtimi I një makinë autonome nuk është e lehtë, por prototipet e ndërtuara deri me tani kanë demonstruar se automjetet autonome mund ta sfidojnë me sukses mjedisin dhe kanë potencial për të ndikuar pozitivisht në jetët e miliona njerëzve
Sistemet e asistences se shoferëve tashme janë paisje të zakonshme në makinat moderne dhe janë duke avancuar cdo vit edhe më shumë. Qëllimi kryesor është të ndihmojnë makinën të udhëtojë më e sigurtë. Qofte ky një paralajmerim për shoferin nëse zvogelon distancen e sigurisë me mjetin që udhëton përpara, dhenia e një sinjali kur kalon në korsine anesore apo parkimi automatik për të shmangur përplasjen me objektet dhe mjetet e parkuara.
një tipar ende shumë I rëndësishëm I makinave autonome është përcaktimi I rrugës që duhet të ndjekin për të shkuar në destinacionin e caktuar, mundësisht pa prekur ndonjë pengese që mund të haset gjatë rrugës , gje e cila mund të realizohet përmes një prej algoritmeve të shumte të machine learning .
Ndoshta avantazhi me I madh që shfaqin këto algorimte inteligjente, është se sjellja e makinës bëhet shumë herë me fleksible, duke ndryshuar në varesi të faktoreve të ndryshem që ndërveprojnë për të realizuar një qëllim specifik. Megjithate, përfitimet nga udhëtimet autonome fillojnë që me reduktimin e ndotjes se zonave urbane përmes menaxhimit të drejtimit të automjetit dhe rendimentit të karburantit, deri në kontrollin e rrjedhes se trafikut dhe problemeve të parkimit. Gjithashtu, mjetet autonome arrijnë të përshpejtojnë transportimin e njerëzve, mallrave, dhe po ashtu përmirsojnë sigurinë duke reduktuar gabimin njerëzor.
The latest technological innovations are creating the base for mobility solutions which, accompanied to cultural and socio-economic changes taking place all over the World, opens the door to new mobility scenarios. The challenge of innovation in transport sector is represented by automation of vehicles, including all transport sectors (road, sea, rail and air) and particularly the automotive from the viewpoint of demonstration and validation. This chapter presents the state of the art of Connected and Automated Vehicles (CAVs) development outlining the conditions for sustainable mobility solutions and new business models for transport services.
The impacts of autonomous vehicles, coupled with greater inter-vehicle and system connectivity, may be
far-reaching on several levels. They entail changes to (1) the demand and behavior side, (2) the supply
of mobility services, and (3) network and facility operational performance. We focus here on their impact
on traffic flow and operations, especially in mixed traffic situations in which autonomous vehicles share the
road with regular, human-driven vehicles, along with connected vehicles that may also have some automated
functions. These mixed traffic situations correspond to likely deployment scenarios of the technologies, especially
in the long transition towards 100% deployment. We explain using elementary traffic science concepts how
autonomous vehicles and connected vehicles are expected to increase the throughput of highway facilities, as
well as improve the stability of the traffic stream. A microsimulation framework featuring varying behavioral
mechanisms for the three classes of vehicles is introduced. The framework is used to examine the throughput
and stability questions through a series of experiments under varying market penetration rates of autonomous
and/or connected vehicles; at low market shares, the impacts are relatively minor on either throughput or
stability. However, as market shares increase, autonomous vehicles exert a greater influence on both dimensions
compared to the same shares of connected vehicles. Applications of the framework to examine the effectiveness
of selected traffic management approaches are discussed, including dedicated lanes for autonomous vehicles
(good only if its use is optional and when the market share of autonomous vehicles is greater than the percentage
of nominal capacity represented by that lane), and speed harmonization.
Self-driving shuttle bus makes its debut at Brussels Airport. Airside International
- W Hayes
Hayes W. (2019). Self-driving shuttle bus makes its debut at Brussels Airport.
Airside International. Retrieved from https://www.airsideint.com/self-drivingshuttle-bus-makes-its-debut-at-brussels-airport/
The Influence of Interface Design on Driver Behavior in Automated Driving. CARSP: The Canadian Association of Road Safety Professionals
- C P Lau
- J L Harbluk
- P C Burns
- Y El-Hage
Lau, C.P., Harbluk, J.L., Burns, P.C., & El-Hage, Y. (2018). The Influence of
Interface Design on Driver Behavior in Automated Driving. CARSP: The
Canadian Association of Road Safety Professionals. Victoria, BC, June 2018.(4)