Research finds that the global market value of cargo bikes will hit 2.4 billion euros by 2031. Analysts with Future Market Insights assessing the growth of cargo bikes have placed the parcel courier industry as a key buyer of electric cargo bikes, forecasting that 43 per cent of sales could go to this industry. This growth is driven by city logistics trends, particularly as studies emerge showing the high efficiency and cost saving of the cargo bike versus the delivery van. It will not solely be direct incentives that drive uptake, however. The policy that restricts motoring and emissions is expected to be a key driver for businesses that seek profitability, with three-wheeled electric cargo bikes making up nearly half the market. The advance of e-bike technology has seen a strong rise in market share for assisted cargo bikes, now accounting for a 73 per cent market share. Potentially limiting the growth is the legislation governing the output and range of electric cargo bikes (FMI, 2021).To deal with the issues of faster delivery, clean delivery (low/zero emission) and less space in dense cities, the light electric freight vehicle (LEFV) can be–and is used more and more as–an innovative solution. The way logistics in urban areas is organized is being challenged, as the global growth of cities leads to more jobs, more businesses and more residents. As a result, companies, workers, residents and visitors demand more goods and produce more waste. More space for logistics activities in and around cities is at odds with the growing need for accommodation for people living and working in cities. Book: Innovations in Transport: Success, Failure and Societal Impacts
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
Intelligent technology in automotive has a disrupting impact on the way modern automobiles are being developed. New technology not only has brought complexity to already existing information in the car (digitization of driver instruments) but also brings new external information to the driver on how to optimize the driving style amongst others from the perspective of communicating with infrastructures (Vehicle to Infrastructure communication (V2I)). The amount of information that a driver has to process in modern vehicles is increasing rapidly due to the introduction of multiple displays and new external information sources. An information overload lies awaiting, yet current Human Machine Interface (HMI) designs and the corresponding legal frameworks lag behind. Currently, many initiatives (Pratijkproef Amsterdam, Concorda) are being developed with respect to V2I, amongst others with Rijkswaterstaat, North Holland and Brabant. In these initiatives, SME’s, like V-Tron, focus on the development of specific V2I hardware. Yet in the field of HMI’s these SME’s need universities (HAN University of Applied Science, Rhine Waal University of Applied Science) and industrial designers (Yellow Chess) to help them with design guidelines and concept HMI’s. We propose to develop first guidelines on possible new human-machine interfaces. Additionally, we will show the advantages of HMI’s that go further than current legal requirements. Therefore, this research will focus on design guidelines averting the information overload. We show two HMI’s that combine regular driver information with V2I information of a Green Light Optimized Speed Advise (GLOSA) use case. The HMI’s will be evaluated on a high level (focus groups and a small simulator study). The KIEM results in two publications. In a plenary meeting with experts, the guidelines and the limitations of current legal requirements will be discussed. The KIEM will lead to a new consortium to extend the research.
The automobile industry is presently going through a rapid transformation towards autonomous driving. Nearly all vehicle manufacturers (such as Mercedes Benz, Tesla, BMW) have commercial products, promising some level of vehicle automation. Even though the safe and reliable introduction of technology depends on the quality standards and certification process, but the focus is primarily on the introduction of (uncertified) technology and not on developing knowledge for certification. Both industry and governments see the lack of knowledge about certification, which can ensure the safety of autonomous technology and thus will guarantee the safety of the driver, passenger, and environment. HAN-AR recognized the lack of knowledge and the need for novel certification methodology for emerging vehicle technology and initiated the PRAUTOCOL project together with its SME partners. The PRAUTOCOL project investigated certification methodology for two use-cases: certification for automated highway overtaking pilot; and certification for automatic valet parking. The PRAUTOCOL research is conducted in two parallel streams: certification of the driver by human factors experts and certification of vehicle by technology experts. The results from both streams are published and presented in respective but limited target groups. Also, an overview of the PRAUTOCOL certification methodology is missing, which can enable its translation to different use-cases of automated technology (other than the used ones). Therefore, to realize a better pass-through of PRAUTOCOL's results to a broader audience, the top-up is required. Firstly, to write a (peer-reviewed) Open Access article, focusing on the application and translation of PRAUTOCOL's methodology to other automated technology use-cases. Secondly, to write a journal article, focusing on the validation of automatic highway overtaking system using naturalistic driving data. Thirdly, to organize a workshop to present PRAUTOCOL's results (valorization) to industrial, research, and government representatives and to discuss a follow-up initiative.
