Towards Connected and Autonomous Vehicle Highways

This chapter serves as an introductory chapter to the book ‘Towards Connected and Autonomous Vehicle Highway: Technical, Security and Social Challenges’. To assist the readers, particularly those who are new to these topics, it also acts to present the preliminary ideas of ‘Autonomous, Connected, Electric and Shared (ACES)’ as the Future of the Mobility sectors. The editors try to provide the background of each of the elements in ACES and briefly identify the issues surrounding their developments, thus subsequently highlighting the justification of having the multi-angle discussions for the book on the said topics.

Reducing fuel consumption is one of the major benefits of platooning. While introducing platooning in mixed traffic, surrounding traffic will interfere with the platoon, risking a loss in fuel savings. In this work, a method for estimating potential fuel loss due to cut-ins in platoons is presented. Based on interviews with truck drivers with experience from platooning, and naturalistic data from previous research, we estimate the potential loss of fuel savings due to cut-ins and compare two scenarios with different amounts of traffic. The results show that platoons spend as much as 20% of time in cut-ins on typical European roads, reducing fuel savings in platooning from 13% down to 10%. Consequently, avoiding cut-ins has a positive environmental effect worth considering.

Smart cities are defined as the municipalities that use information and communication technologies to collect data, share information, increase efficiency, and, most importantly, improve the quality of its citizens’ life. Smart transportation is one of the key factors in a successful smart city. It can be supported by several factors including zero-emission electric vehicles, safer, more efficient public transportation, less congested roads, safe walkable sidewalks and crosswalks, less daily commute, in addition to high-tech technologies, and, finally, smart connected autonomous vehicles.

This chapter aims at examining possible design configurations of the new and smart cities, especially with autonomous and connected vehicles to maintain safety, sustainability, and walkability in the city while implementing such technologies. Such examination is reflected through a reimagination of an existing main road in the city of Dubai, which is expecting autonomous vehicles on the road in its 2030 city plan.

Road traffic congestion is one of the challenging problems confronting city dwellers globally. It is majorly caused by either one or a combination of recurrent congestion, nonrecurrent congestion, and precongestion conditions in urban road networks. This chapter performs a bibliometric analysis and reviews the volume of literature linking big data with combating road traffic congestion between 2011 and 2020. The review employs a quantitative analysis of bibliometric science mapping tool to highlight features that affect knowledge accumulation. The chapter also reviews the intellectual structure of knowledge based on total publications and citations. The key scholars, documents, affiliations, regions, data, and algorithms that influenced the development of this research area are analyzed. The results of documents co-citation evaluation show that the key research clusters are salient elements linked with the development and deployment of connected and autonomous vehicles (CAVs) technology. These research clusters are traffic flow prediction, congestion and accidents alert systems, security and privacy mitigation, vehicle emission profiles, travel time estimation, optimization of vehicular routing, journey planning and congestion prediction, and travel and parking guidance. Finally, the chapter presents the way forward and future research direction for sustainable road traffic management in the context of smart city initiatives leveraging on big data.

Smart cities are not any more waves of the future, they are more being actualities we live and witness every day. Smart cities are erected by the integration of Smart components into our systems and the way we promote this integration to produce the desired outcome of the efficiency and stability of these practicalities, the closer we are to form Smart cities. The intelligent amalgamation and interaction between Smart transportation systems and Smart people are discussed thoughtfully in this chapter, where we propose a promoting mutual version of an innovative Smart transportation system to act as an intelligent learning object-based transportation system in quest of the real time. The interacting proposal meets the real-time needs and requirements of the Smart people. The chapter proposes a mutual design of the Me-Online mobile application that promotes a simple planned trip into the fourth dimension of gratification where Smart people request a fulfillment journey supported with all logistics support they desire to have while taking the ride, such as choosing a partner, picking a coffee from close stay-at-home/small businesses along with being online interacting and updated with social network-based recommending systems while ongoing. The application is expected to increase the joyful of the ride, make decisions, raise, and refresh the stand-alone businesses available close to the chosen route. All this while still taking the financial side into consideration that keeps the smartness of having a journey with minimum fees. The successful trip is considered a template (transportation learning object) that can be reused, updated, and customized by others who like the content.

The paradigm communicate with anyone anywhere at anytime nowadays spans to cyber-physical systems in general including automotive industry. The established path and goal for the automotive industry include connected cars and various services, like autonomous driving. Communication technologies used with connected cars fall into one umbrella, called vehicle-to-everything (V2X). Different sensors and processing units inside connected cars communicate and synchronize using different types of intra-vehicle protocols. In addition to in-vehicle communication, connected cars communicate and interact with their environment, using so-called inter-vehicle protocols. Because of significant advancements in V2X technology, security issues related to them are on the rise. The security-by-design frameworks, including threat modeling and formal methods, have the potential and means to answer these challenges. This chapter contains a comprehensive, security focused, overview of the connected cars’ communication architecture and the most important protocols. Then it discusses security-by-design frameworks application within this domain – threat modeling state of the art methodologies and the ability to adapt those for the automotive industry, and formal verification tools and their applications in V2X protocols space. Furthermore, challenges and future research directions are discussed.

Autonomous vehicles (AVs) are increasingly proposed as a solution towards addressing urbanisation challenges in smart city initiatives, such as congestion, pollution, road safety and transport accessibility. However, their socio-economic and environmental benefits can be hampered by new technological risks emerging from their use. This chapter explores some of the major risks associated with AV adoption that need to be addressed to reap the technology’s full benefits. AVs can introduce safety risks arising from technical issues in the AV system and ethical issues in their design and deployment. In addition, socio-economic equity is a key aspect of sustainability, which can be undermined by AVs displacing jobs in existing industries, as well as by their discriminatory driving decisions shaped by algorithmic biases and the value-laden design choices of AV stakeholders. AVs’ connected nature also poses privacy and cybersecurity risks that can dampen consumer acceptance. After exploring these issues, we discuss some of the governance strategies adopted to address these risks and highlight the gaps in research and practice that need to be addressed.

Development cycles in Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) technologies pose various challenges even at the development stages, since they require extensive calibration, testing, and validation before they can be commissioned. Specifically, testing and verifying the expected vehicle behavior for ADAS/AD functions in various traffic scenarios is a major challenge. Unfortunately, the extent of real-life tests is often quite limited due to associated high costs. Therefore, it is a common practice to employ simulations for this. However, simulation-only verification also has limitations due to un-modeled dynamics. It is therefore in the interest of the developers and OEMs alike to ensure that ADAS/AD systems conform to reliability, compliance, and performance requirements in a cost-effective and efficient manner. Motivated by these, we introduce in this chapter two alternative testing/verification methods combining real-world testing with simulation for ADAS/AD development. The first concept involves a novel steerable chassis dynamometer and real-time camera stimulation in a co-simulation framework. The second involves a generalization of the co-simulation framework with a vehicle-in-the-loop concept and is named “hybrid testing” that was developed in the scope of the EU-H2020 funded project INFRAMIX, where a real vehicle can be combined with a virtual traffic scenario.

Autonomous driving is becoming increasingly popular worldwide. Currently, there is a high number of successful use cases and pilot projects around the world, but the market solutions are limited to restricted areas and advanced driving assistance systems. The main reason is the challenging shift from simulation and test tracks to the real world, where the number of possible scenarios increases exponentially, thus increasing the complexity of the driving models. In consideration of such a complex environment, this chapter focuses on the description of the real-world challenges in connection to running pilots within the Sohjoa Baltic project. Sohjoa Baltic aims at piloting electric autonomous minibuses in several cities of the Baltic sea region, constituting a challenging testbed compared to other cities piloting autonomous driving. The chapter gives a scientific interpretation to a series of practical problems, describing the challenges encountered during the running pilots in the Sohjoa Baltic project, reviewing state-of-the-art solutions and future implementation ideas that can be used on each vehicle or in the infrastructure.

The vehicular industry is evolving towards unmanned and connected vehicles that offer numerous benefits such as traffic congestion control and improved road safety. Lately, a considerable progress has been made in the radio access technologies for vehicular communication, including Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) technologies. An extensive set of related pilot use-cases have been developed, each pilot use-case with its particular requirement. In this chapter, we have developed and analyzed set of pilot use-cases for the vehicular environment (V2V & V2I) that involves exchanging road weather information between vehicles considering transport layer protocols, TCP and UDP. Here, we have focused on the Internet Protocol (IP) layer that contains the information including prefix and IP address of an external network interface for the internetworking between vehicles and infrastructure. In VANET’s, TCP is generally not employed, which leaves UDP as the only viable substitute within the standard IP stack. Pilot measurements have been performed on a test track in Sodankylä, Finland, owned by the Finnish Meteorological Institute (FMI). Both IEEE-802.11p and cellular 5G test network have been used for this performance analysis considering TCP and UDP. The performance of IEEE-802.11p and 5G technology is highly dependent on the distance and defined parameter settings. In this performance evaluation, the network layer protocols (TCP/UDP) were majorly analyzed by considering throughput, data rate, packet loss, and network latency. In our comparison, results indicated that UDP performs better than TCP at the cost of low network reliability with less latency. With a less queuing delay but high bit packet loss percentage (%), and in contrast to TCP, UDP performance was better to exchange real-time road weather data in V2V and V2I scenarios. These pilot use-cases provide us a deep insight into the network performance of intelligent traffic infrastructure exchanging road weather data. The performance evaluation in this chapter would help to improve the vehicular networking with increased Intelligent Transport System (ITS) efficiency.

Autonomous shuttle development has gained popularity as one of the research development areas in autonomous vehicle field. In this chapter, the shuttle development in Universiti Malaysia Pahang is highlighted, while its vehicle simulation environment is developed to mimic the real environment of the university which consists of many roundabout junctions. The roundabout environment is constructed in vehicle simulator for data logging and testing and then published to the ROS network. Point cloud matrices from different moving frames are stitched using iterative closest point (ICP) algorithm to form a final useful map for further post-processing. The ICP algorithm performance is shown with different number of stitching frames and the results show that the algorithm is capable to show a reliable single map from different point cloud frames.

Autonomous driving requires object recognition for vehicles to automatically generate a path according to their recognised environment, the conditions of which have different dims of light, from daylight to night. High-resolution images require high amounts of expensive storage as automated driving moves from urban to rural areas, where driving at night and recognising traffic signs and lights are necessary for all light conditions. Therefore, a reliable source of input, allowing for the intended performance of an autonomous driving system such as the country or rural road pilot, is necessary for adequate deployment of its functionality in its target environment. For quality criteria such as intended performance, functional reliability, safety, and correct driving behaviour are to be ensured; accuracy metrics can be a substantial contribution to the product quality criteria. Furthermore, since autonomous technology faces the challenge of being costly, thus any new innovative methods for saving costs, without comprising quality, would help to develop and enhance the chance of this developing technology being installed into more advanced automated or autonomous driving vehicles once the product safety as quality criteria can be validated on target roads. Part of this work’s limitation was that only a simulation environment was used for testing the image processing and autonomous driving accuracy models. Through research, certain algorithms were found that may be used in storage size minimisation for taking a high-resolution image; its size had to be reduced for use without compromising accuracy in the classification process. Further research in their validation may be necessary.