Active mobility, such as biking, faces a common challenge in Swedish municipalities due to the lack of adequate lighting during the dark winter months. Insufficient lighting infrastructure hinders individuals from choosing bicycles, despite the presence of well-maintained bike paths and a willingness to cycle. To address this issue, a project has been undertaken in the Swedish municipality of Skara for an alternative lighting solution using drones. A series of tests have been conducted based on drone prototypes developed for the selected bike paths. Participants were invited to cycle in darkness illuminated by drone lighting and share their mobility preferences and perception. This paper summarizes the users’ perception of drone lighting as an alternative to fixed lighting on bike paths, with a special focus on the impact on travel habits and the perceived sense of security and comfort. Most participants were regular cyclists who cited bad weather, time, and darkness as significant factors that deterred them from using bicycles more frequently, reducing their sense of security. With drone lighting, the participants appreciated the illumination’s moonlight-like quality and its ability to enhance their sense of security by illuminating the surroundings. On the technology side, they gave feedback on reducing the drone’s sound and addressing lighting stability issues. In summary, the test results showcase the potential of drone lighting as a viable alternative to traditional fixed lighting infrastructure, offering improved traffic safety, sense of security, and comfort. The results show the feasibility and effectiveness of this innovative approach, supporting transformation towards active and sustainable mobility, particularly in regions facing lighting challenges.

With the emerging connected automated vehicles, 5G and Internet of Things (IoT), vehicles and road infrastructure become connected and cooperative, enabling Cooperative Intelligent Transport Systems (C-ITS). C-ITS are transport system of systems that involves many stakeholders from different sectors. While running their own systems and providing services independently, stakeholders cooperate with each other for improving the overall transport performance such as safety, efficiency and sustainability. Massive information on road and traffic is already available and provided through standard services with different protocols. By reusing and composing the available heterogeneous services, novel value-added applications can be developed. This paper introduces a choreography-based service composition platform, i.e. the CHOReVOLUTION Integrated Development and Runtime Environment (IDRE), and it reports on how the IDRE has been successfully exploited to accelerate the reuse-based development of a choreography-based Urban Traffic Coordination (UTC) application. The UTC application takes the shape of eco-driving services that through real-time eco-route evaluation assist the drivers for the most eco-friendly and comfortable driving experience. The eco-driving services are realized through choreography and they are exploited through a mobile app for online navigation. From specification to deployment to execution, the CHOReVOLUTION IDRE has been exploited to support the realization of the UTC application by automatizing the generation of the distributed logic to properly bind, coordinate and adapt the interactions of the involved parties. The benefits brought by CHOReVOLUTION IDRE have been assessed through the evaluation of a set of Key Performance Indicators (KPIs).

An interaction protocol for cooperative platoon merge on highways is proposed. The interaction protocol facilitates a challenge scenario for the Grand Cooperative Driving Challenge (GCDC) 2016, where two platoons running on separate lanes merge into one platoon due to a roadwork in one of the lanes. Detailed interaction procedures, described with state machines of each vehicle are presented. A communication message set is designed to support platoon controllers to perform safe and efficient manoeuvres.

In the picturesque municipality of Skara, Sweden, an innovative endeavor named the Skara Skyddsängel project embarked on a mission to revolutionize the concept of nighttime cycling safety. Tackling the challenge of inadequate lighting on bike paths, especially during the long, dark Swedish winters, this project proposed a novel solution: the use of drones for dynamic, on-demand bicycle path illumination. Aimed at enhancing safety and comfort for cyclists, this initiative stood at the intersection of technological innovation, sustainable mobility, and community welfare. The project's journey began with meticulous planning and development of a drone lighting system, featuring commercial drones equipped with powerful LED spotlights. The technical prowess of these drones was matched with a suite of sophisticated software for precise navigation and cyclist tracking. The endeavor then extended into the realm of user experience. A series of user studies, including initial pilot tests, focus groups, and extensive field trials, were conducted. Participants first experienced the innovative lighting in a virtual setting with VR headsets. After further improvements, cyclists were invited and test the solution in real-world settings, providing invaluable feedback. The feedback from these user studies painted a picture of success and potential. Most participants expressed a sense of increased safety and comfort under the watchful illumination of the drones. The lighting was deemed effective, with its broad and strong coverage enhancing visibility significantly. While the technology showed immense promise, it also pointed out areas for refinement, such as improving drone stability and light distribution. The project's reach extended beyond the confines of Skara through an aggressive dissemination campaign. It garnered attention through press releases, was a subject of discussion in various media outlets, and featured prominently in events like Kista Mobility Day in Stockholm and the GMC Global Mobility Call in Spain. The project's milestones and findings were also shared at international conferences, including the prestigious Human-Computer Interaction International (HCII) 2023, the annual POLIS conference 2023, as well as Transportforum 2024. The Skara Skyddsängel project stands as a testament to the potential of integrating advanced technology into everyday life to enhance safety and promote sustainable practices. It underscores the feasibility of drone-based lighting solutions as a viable alternative to traditional infrastructure, particularly in resource-constrained settings. As the project transitions from concept to reality, it highlights the importance of continued innovation, regulatory considerations, and public engagement. The road ahead calls for further technical enhancements, broader user studies, and a deep dive into the regulatory and ethical aspects of drone usage, as well as economic analysis. In conclusion, the Skara Skyddsängel project is not just about bringing light to dark paths; it's about envisioning a future where technology and human-centric design converge to create safer, more inclusive, and sustainable communities. The project not only illuminates bike paths but also lights the way for future endeavors aiming to merge technology with public welfare.

With the fifth generation (5G) communication technologies on the horizon, the society is rapidly transformed into a fully connected world. The Future Internet (FI) is foreseeable to consist of an infinite number of software components and things that coordinate with each other to enable different applications. Transport systems, as one of the most important systems in future smart cities, will embrace the connectivity, together with the fast development of cooperative and automated vehicles to enable smart traffic. To facilitate this transformation, a service choreography composition platform is under development to enable fast innovation and prototyping of choreography-based Internet of Things (IoT) applications by automatically synthesizing choreographies. Based on the method, a smart traffic application is developed and demonstrated.

Intersection management is one of the most challenging problems within the transport system. Traffic light-based methods have been efficient but are not able to deal with the growing mobility and social challenges. On the other hand, the advancements of automation and communications have enabled cooperative intersection management, where road users, infrastructure, and traffic control centers are able to communicate and coordinate the traffic safely and efficiently. Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter. Cooperative methods, including time slots and space reservation, trajectory planning, and virtual traffic lights, are discussed in detail. Vehicle collision warning and avoidance methods are discussed to deal with uncertainties. Concerning vulnerable road users, pedestrian collision avoidance methods are discussed. In addition, an introduction to major projects related to cooperative intersection management is presented. A further discussion of the presented works is given with highlights of future research topics. This paper serves as a comprehensive survey of the field, aiming at stimulating new methods and accelerating the advancement of automated and cooperative intersections.

With intensive research and field operational tests over the intelligent transportation area and the advancements of information and communication technologies, intelligent transportation systems reach the stage of deployment. EU focuses on cooperative intelligent transportation systems and confirms the finalization of the first release of the standards, paving the way for deployment in the coming years. This paper presents the concept of EU's cooperative intelligent transportation systems and describes in detail the functional architecture, together with highlights of related standardsthat have been finalized in Release 1. Latest updates of the cooperative intelligent transportation systems are provided for both industry and academia, aiming at helping to accelerate cooperative mobility.

Emergency management has long been recognized as a social challenge due to the criticality of the response time. In emergency situations such as severe traffic accidents, minimizing the response time, which requires close collaborations between all stakeholders involved and distributed intelligence support, leads to greater survival chance of the injured. However, the current response system is far from efficient, despite the rapid development of information and communication technologies. This paper presents an automated collaboration framework for emergency management that coordinates all stakeholders within the emergency response system and fully automates the rescue process. Applying the concept of multiaccess edge computing architecture, as well as choreography of the service oriented architecture, the system allows seamless coordination between multiple organizations in a distributed way through standard web services. A service choreography is designed to globally model the emergency management process from the time an accident occurs until the rescue is finished. The choreography can be synthesized to generate detailed specification on peer-to-peer interaction logic, and then the specification can be enacted and deployed on cloud infrastructures.

Despite the existing regulation efforts and measures, vehicles with dangerous goods still pose significant risks on public safety, especially in road tunnels. Solutions based on cooperative intelligent transportation system (C-ITS) are promising measures, however, they have received limited attention. We propose C-ITS applications that coordinate dangerous goods vehicles to minimize the risk by maintaining safe distances between them in road tunnels. Different mechanisms, including global centralized coordination, global distributed coordination, and local coordination, are proposed and investigated. A preliminary simulation is performed and demonstrates their effectiveness.

The traffic system is transforming into a highly complex system of systems with increasing connectivity and automation. Engineering such a system of systems requires close interaction between related stakeholders including authorities, car manufacturers, and the service and technology providers, both from the organizational and technical perspective. This paper describes a cloud-based traffic control system that provides a platform to support cross-sector interoperable information sharing, and data intelligence for future connected and autonomous vehicle integration. The system is engineered from a system of systems perspective with multi-stakeholder engagement and is designed to be cloud-native for stakeholder and service scalability. The paper discusses the motivation of the system, followed by a detailed description on the system architecture and the constituent systems. Supported services are presented with their working process, information flow, as well as their public demonstrations.

This pre-study aims at analyzing the needs of digital infrastructure for connected and automated transport systems in Sweden and building roadmaps with concrete actions for accelerating the implementation of digital infrastructure. The pre-study has conducted extensive literature studies on projects, activities, and policies related to digital infrastructure in Sweden, the EU, and globally. The pre-study focuses on supporting existing activities, goals, and roadmaps within the Swedish transport sector and follows closely the roadmap for a connected and automated road transport system from Trafikverket. Expert interviews with both public and private stakeholders have been conducted to collect opinions and to formulate concrete actions. The report consists of mainly two parts, the description of physical and digital transport infrastructure, and the identified focus areas, roadmaps, and action plans. Also, state-of-the-art digital transport infrastructure is given as an appendix to support the roadmap. The report describes physical digital transport infrastructure as the IT, communication, and data infrastructure that together with regulations and standards enables interoperable and digital cooperation between connected vehicles, people, infrastructure, and other data sources. It is a system of systems that requires both bottom-up and top-down approaches with balanced public and private investments. A layered description is given including the communication infrastructure, transport data eco-system, applications and services, and organizational partnership. The report proposes the establishment of a long-term public-private partnership platform to join forces to accelerate the implementation of physical digital transport infrastructure. The platform requires strong engagement from all stakeholders for addressing common challenges and for stimulating innovative policies and business models. As a trigger, eight focus areas are proposed including connectivity, positioning, control tower, data, architecture, evaluation, policies and regulations, and business models. For each area, the roadmap describes in detail the actions that should be taken together with the expected results for the year 2021 to 2025. The transport system is a system of systems that evolves fast. Stakeholder roles and responsibilities may change with new business models and new actors emerge all the time. To accommodate such evolution, the roadmap proposes iterative development processes. The proposed actions are by no means fixed, instead, the stakeholders need to yearly conduct evaluations, agree on prioritized tasks and expected results, identify gaps, and decide on new prioritized activities. The pre-study is financed by the strategic innovation program Drive Sweden, which is a joint investment by Vinnova, Formas and Energimyndigheten, and Trafikverket through the project Plattform för snabb och effektiv implementering av digital infrastruktur för transportsystem with project number 2019-04787

Despite the strong interests in creating data economy, automotive industries are creating data silos with each stakeholder maintaining its own data cloud. Federated learning (FL), designed for privacy-preserving collaborative Machine Learning (ML), offers a promising method that allows multiple stakeholders to share information through ML models without the exposure of raw data, thus natively protecting privacy. Motivated by the strong need for automotive collaboration and the advancement of FL, this paper investigates how FL could enable privacy-preserving information sharing for automotive industries. We first introduce the statuses and challenges for automotive data sharing, followed by a brief introduction to FL. We then present a comprehensive discussion on potential applications of federated learning to enable an automotive collaborative ecosystem. To illustrate the benefits, we apply FL for driver action classification and demonstrate the potential for collaborative machine learning without data sharing.

Emergency response system is a complex system of systems (SoS). The introduction of connected and autonomous vehicles (CAVs) introduces an extra dimension into the complexity. Future emergency response must be able to take into account of the autonomous vehicles with different automation levels and leverage the increasing connectivity and automation for efficient emergency response. Architecture frameworks have long been used for system engineering for large complex systems. The emerging unified architecture framework converges previous architecture frameworks for a unified one towards both military and civilian use. Based on the scenario of emergency response with CAVs on highways, this paper motivates an enterprise architecture for emergency response system of systems (ERSoS) with identification of the key challenges and opportunities in addition to a proposal of required capabilities. The work is a first iteration of an enterprise architecture for ERSoS with CAVs and forms part of the overall ERSoS architecture development process. 

With the rapid development of artificial intelligence technology, the deep learning method has been introduced for vehicle trajectory prediction in the internet of vehicles, since it provides relative accurate prediction results, which is one of the critical links to guarantee security in the distributed mixed-driving scenario. In order to further enhance prediction accuracy by making full utilization of complex traffic scenes, an improved multimodal trajectory prediction method based on deep inverse reinforcement learning is proposed. Firstly, a fused dilated convolution module for better extracting raster features is introduced into the existing multimodal trajectory prediction network backbone. Then, a reward update policy with inferred goals is improved by learning the state rewards of goals and paths separately instead of original complex rewards, which can reduce the requirement for predefined goal states. Furthermore, a correction factor is introduced in the existing trajectory generator module, which can better generate diverse trajectories by penalizing trajectories with little difference. Abundant experiments on the current popular public dataset indicate that the prediction results of our proposed method are a better fit with the basic structure of the given traffic scenario in a long-term prediction range, which verifies the effectiveness of our proposed method. © 2022 by the authors.

The Grand Cooperative Driving Challenge (GCDC), with the aim to boost the introduction of cooperative automated vehicles by means of wireless communication, is presented. Experiences from the previous edition of GCDC, which was held in Helmond in the Netherlands in 2011, are summarized, and an overview and expectations of the challenges in the 2016 edition are discussed. Two challenge scenarios, cooperative platoon merge and cooperative intersection passing, are specified and presented. One demonstration scenario for emergency vehicles is designed to showcase the benefits of cooperative driving. Communications closely follow the newly published cooperative intelligent transport system standards, while interaction protocols are designed for each of the scenarios. For the purpose of interoperability testing, an interactive testing tool is designed and presented. A general summary of the requirements on teams for participating in the challenge is also presented.

Current transportation systems face great challenges due to the increasing mobility. Traffic accidents, congestion, air pollution, etc., are all calling for new methods to improve the transportation system. With the US legislation in progress over vehicle communications and EU’s finalization of the basic set of standards over cooperative intelligent transportation systems (C-ITS), vehicular ad hoc network (VANET) based applications are expected to address those challenges and provide solutions for a safer, more efficient and sustainable future intelligent transportation systems (ITS). In this chapter, transportation challenges are firstly summarized in respect of safety, efficiency, environmental threat, etc. A brief introduction of the VANET is discussed along with state of the art of VANET-based applications. Based on the current progress and the development trend of VANET, a number of new features of future VANET are identified, together with a set of potential future ITS applications. The on-going research and field operational test projects, which are the major enabling efforts for the future VANET-based C-ITS, are presented. The chapter is of great interest to readers working within ITS for current development status and future trend within the C-ITS area. It is also of interest to general public for an overview of the VANET enabled future transportation system.

Cooperative speed harmonization based on floating car data aiming at improving manoeuvrability in a highly utilized intersection is presented. Cooperative Intelligent Transportation Systems (C-ITS) aims at gather information about the current traffic situation based on wireless communication and provide aggregated information back to the road users in order to improve e.g. efficiency, safety and/or comfort. Simulations show that the proposed speed harmonization application is capable of lowering the CO2 emissions with up to 11%, increasing the average speed with up to 14% and reducing the travel time with up to 16% for all vehicles in the simulation. It is also found that not only the cooperative vehicles benefit from the application but also the non-equipped vehicles. Furthermore, the cooperative traffic simulator has been shown to be a valuable tool for investigating how C-ITS applications may be utilized to develop future traffic system.

Foreword All transport systems have a certain capacity determined by its configurations. For cars the most efficient current form is constant speed driving, e.g. the motorway. Its capacity is limited by the time separation between vehicles. Any transport system that stops because of congestion or other causes by definition sees its capacity reduced to zero. Hence traffic jams are hugely disruptive. Public transport operates on a model inherited from the 19 th Century. Vehicles (buses, trams, railways, metros) run on a regular (timetabled) basis and stops at every station (bus stop). Since there is no pre-booking and the need of transport is hard to foresee, the vehicles are often almost empty, at other times hugely congested. The NuMo technology emerges from decades of work across the whole transportation industry. Autonomous electric vehicles (AEVs) equipped with vehicle-to-vehicle (V2V) communication can safely keep shorter distances. In practical terms this means that a platooned car system has the same capacity in one lane as a double-lane motorway. Automated intelligent controls ensure that the NuMo systems never stops, thus achieving the highest capacity. Instead of waiting for the mass deployment of fully automated vehicles, NuMo starts with dedicated networks that integrate tightly with existing infrastructure for step-wise smooth transition to fully automated transport system. NuMo includes an on-demand public transport system which only runs when it is needed. The system will take advantage of close-spacing possible with robot controls – vehicles can run close together and also use less road width by less wiggling. Equally importantly stations and access to the normal road network is arranged such that the traffic flow never stops. The urban impact can be imagined by understanding the impact of modern public transport systems currently under construction. Some of them are underground to avoid disrupting the street patterns. Some are elevated, some rely on physical separation at grade. One interesting option is to use tunnels underground or in water to further reduce disruption. Many cities are abandoning the traditional port infrastructure giving huge opportunities to again regard water as a connector rather than something to cross. The NuMo system uses all of those techniques and detailed design studies are under way for each of those options. NuMo will make an important contribution to environmental sustainability in many respects. Firstly, it will accelerate adoption of electric propulsion; secondly it will encourage vehicle sharing; and thirdly by only running when needed will save on unnecessary movements and finally its construction costs will be less than conventional systems. Sketches of NuMo networks are presented on places as diverse as Stockholm, Gothenburg and New York. Naturally the system will also be crucial in the development of new cities. This report is a summary of the studies performed within the project “New urban infrastructure support for autonomous vehicles” financed by Vinnova through the Strategic Innovation Program InfraSweden2030. The aim is to explore the infrastructure support to accelerate the introduction of autonomous electric vehicles for future mobility.

Two-step aggregation is introduced to facilitate scalable federated learning (SFL) over passive optical networks (PONs). Results reveal that the SFL keeps the required PON upstream bandwidth constant regardless of the number of involved clients, while bringing 10% learning accuracy improvement. © 2021 OSA.

The fifth generation (5G) mobile communications system is envisioned to serve various mission-critical services such as industrial automation, cloud robotics, and safety-critical vehicular communications. To satisfy the stringent end-to-end latency requirement of these services, fog computing has been regarded as a promising technology to be integrated into 5G networks, in which computing, storage, and network functions are provisioned close to end users, thus significantly reducing the latency caused in transport networks. However, in the context of fog-computing-enabled 5G networks, the high mobility feature of users brings critical challenges to satisfy the stringent quality of service requirements. To address this issue, service migration, which transmits the associated services from the current fog server to the target one to follow the users' travel trace and keep the service continuity, has been considered. However, service migration cannot always be completed immediately and may lead to a situation where users experience a loss of service access. In this regard, low-latency service migration plays a key role to reduce the negative effects on services being migrated. In this paper, the factors that affect the performance of service migration are analyzed. To enable low-latency service migration, three main enabling technologies are reviewed, including migration strategies, low-latency, and high-capacity mobile backhaul network design, and adaptive resource allocation. Based on a summary of the reviewed technologies, we conclude that dynamic resource allocation is the worthiest one to research. Therefore, we carry out a use case, where reinforcement learning (RL) is adopted for autonomous bandwidth allocation in support of low-latency service migration in a dynamic traffic environment and evaluate its performance compared to two benchmarks. The simulation demonstrates that the RL-based algorithm is able to self-adapt to a dynamic traffic environment and gets converged performance, which has an obviously smaller impact on non-migration traffic than the two benchmarks while keeping the migration success probability high. Meanwhile, unlike the benchmarks, the RL-based method shows performance fluctuations before getting converged, which may cause unstable system performance. It calls for future research on advanced smart policies that can get convergence quickly, particularly for handling the migration of latency-sensitive services in 5G transport networks. 

During federated learning (FL) process, each client needs to periodically upload local model parameters and download global model parameters to/from the central server, thus requires efficient communications. Meanwhile, passive optical network (PON) is promising to support fog computing where FL tasks can be executed and the traffic generated by FL needs to be transmitted together with other types of traffic for broadband access. In this letter, a bandwidth slicing algorithm in PONs is introduced for efficient FL, in which bandwidth is reserved for the involved ONUs collaboratively and mapped into each polling cycle. Results reveal that the proposed bandwidth slicing significantly improves training efficiency while achieving good learning accuracy for the FL task running over the PON. 

Fog computing is expected to be integrated with communication infrastructure, giving rise to the con­cept of fog-enhanced radio access networks (FeRANs) to support various mission-critical applications. Such archi­tecture brings computation capabilities closer to end users, thereby reducing the communication latency to access ser­vices. In the context of FeRAN, service migration is needed to tackle limited resources in a single fog node and to pro­vide continuous service for mobile end users. To support service migration, high capacity and low latency are required in mobile backhaul networks. Passive optical net­works can be a promising solution for such mobile back-haul, in which bandwidth is shared by both migration traffic and that which is not associated with service migra­tion. In this paper, we propose a bandwidth slicing mecha­nism, in which the bandwidth can be provisioned to the migration traffic and non-migration traffic dynamically and effectively to meet their different delay requirements. Simulation results verify that the proposed delay-aware bandwidth slicing scheme can handle the migration traffic properly, i.e., sending it within a required time threshold, while limiting the impact of the migration traffic on the latency and jitter of the non-migration traffic, particularly that with high priority.

Driven by the increasing number of connected vehicles and related services, powerful communication and computation capabilities are needed for vehicular communications, especially for real-time and safety-related applications. A cellular network consists of radio access technologies, including the current long-term evolution (LTE), the LTE advanced, and the forthcoming 5th generation mobile communication systems. It covers large areas and has the ability to provide high data rate and low latency communication services to mobile users. It is considered the most promising access technology to support real-time vehicular communications. Meanwhile, fog is an emerging architecture for computing, storage, and networking, in which fog nodes can be deployed at base stations to deliver cloud services close to vehicular users. In fog computing-enabled cellular networks, mobility is one of the most critical challenges for vehicular communications to maintain the service continuity and to satisfy the stringent service requirements, especially when the computing and storage resources are limited at the fog nodes. Service migration, relocating services from one fog server to another in a dynamic manner, has been proposed as an effective solution to the mobility problem. To support service migration, both computation and communication techniques need to be considered. Given the importance of protocol design to support the mobility of the vehicles and maintain high network performance, in this paper, we investigate the service migration in the fog computing-enabled cellular networks. We propose a quality-of-service aware scheme based on the existing handover procedures to support the real-time vehicular services. A case study based on a realistic vehicle mobility pattern for Luxembourg scenario is carried out, where the proposed scheme, as well as the benchmarks, are compared by analyzing latency and reliability as well as migration cost.

Autonomous vehicles (AVs) are becoming a reality and may integrate with existing public transport systems to enable the new generation of autonomous public transport. It is vital to understand what are the alternatives for AV integration from different angles such as costs, emissions, and transport performance. With the aim to support AV integration in public transport, this paper takes a typical European city as a case study for analyzing the impacts of different AV integration alternatives. A transport planning model considering AVs is developed and implemented, with a methodology to estimate the costs of the transport network. Traffic simulations are conducted to derive key variables related to AVs. An optimization process is introduced for identifying the optimal network configuration based on a given AV integration strategy, followed by the design of different AV integration scenarios, simulation, and analyses. With the proposed method, a case study is done for the city of Uppsala with presentation of detailed cost results together with key traffic statistics such as mode share. The results show that integrating AVs into public transport does not necessarily improve the overall cost efficiency. Based on the results and considering the long transition period to fully autonomous vehicles, it is recommended that public transport should consider a gradual introduction of AVs with more detailed analysis on different combination and integration alternatives of bus services and AVs.

Traffic merge on express-ways creates bottlenecks for traffic flow that potentially lead to traffic jams, especially in dense traffic. Cooperative speed harmonization (CSH), where vehicles are grouped and associated to virtual speed waves for group-wise joining at intersections, is proven to be efficient for on-ramp traffic merge. Based on CSH and considering variations of traffic density from joining roads, an adaptive wavelength adjustment (AWLA) mechanism is proposed in this paper. AWLA extends CSH by dynamically adjusting the length of segments in virtual waves according to the traffic densities of the joining roads. Therefore, roads with denser traffic may have larger segments to carry more vehicles through the intersection, which may improve the overall performance. Simulation results have shown that AWLA can achieve lower CO2 emissions and shorter travel time compared to CSH with static segment settings.

This paper presents the architecture of an Interactive Test Tool (ITT) for interoperability testing of Cooperative Intelligent Transport Systems (C-ITS). Cooperative systems are developed by different manufacturers at different locations, which makes interoperability testing a tedious task. Up until now, interoperability testing is performed during physical meetings where the C-ITS devices are placed within range of wireless communication, and messages are exchanged. The ITT allows distributed (e.g. over the Internet) interoperability testing starting from the network Transport Layer and all the way up to the Application Layer, e.g. to platooning. ITT clients can be implemented as Hardware-in-the-Loop, thus allowing to combine physical and virtual vehicles. Since the ITT considers each client as a black box, manufacturers can test together without revealing internal implementations to each other.

The architecture of the ITT allows users to easily switch between physical wireless networking and virtual ITT networking. Therefore, only one implementation of the ITS communication stack is required for both development and testing. This reduces the work overhead and ensures that the stack that is used during the testing is the one deployed in the real world. 

To promise a high degree of safety in complex mixed-traffic scenarios alongside human-driven vehicles, accurately predicting the maneuvers of surrounding vehicles and their future positions is a critical task and attracts much attention. However, most existing studies focus on reasoning about positional information based on objective historical trajectories without fully considering the heterogeneity of driving behaviors. Besides, previous works have focused more on improving models’ accuracy than investigating their interpretability to explore the extent to which a cause and effect can be observed within a system. Therefore, this article proposes a personalized trajectory prediction framework that integrates driving behavior feature representation to account for driver heterogeneity. Specifically, based on a certain length of historical trajectory data, the situation-specific driving preferences of each driver are identified, where key driving behavior feature vectors are extracted to characterize heterogeneity in driving behavior among different drivers. The proposed LSTMMD-DBV (long short-term memory and mixture density networks with driving behavior vectors) framework integrates driving behavior feature representations into a long short-term memory encoder–decoder network to investigate its feasibility and validate its effectiveness in enhancing predictive model performance. Finally, the Shapley Additive Explanations method interprets the trained model for predictions. After experimental analysis, the results indicate that the proposed model can generate probabilistic future trajectories with remarkably improved predictions compared to existing benchmark models. Moreover, the results confirm that the additional input of driving behavior feature vectors representing the heterogeneity of ­driving behavior could provide more information and, thus, contribute to improving prediction accuracy.

As one of the innovative technologies of intelligent transportation systems (ITS), Connected and Autonomous Vehicles (CAVs) have been deployed gradually. Given that there will be a long transition period before reaching a fully CAVs environment, it is crucial to assess the potential impacts of CAVs on mixed traffic flow. Considering platoon formation process, this study develops a platoon cooperation strategy based on “catch-up” mechanism, and then analyzes the impact on fundamental diagram, traffic oscillation, and traffic safety within mixed traffic. Simulation results show that with an increasing market penetration rate (MPR) of CAVs, road capacity shows an increasing trend. Compared with base scenario, a clear increase in road capacity is also observed under platoon scenario. With an increasing MPR, traffic oscillation is shown to reduce largely. Furthermore, the proposed platoon strategy could dampen frequent shockwaves and shorten the propagation range of waves. Regarding traffic safety, multiple surrogate safety measures (SSMs) are used to evaluate the traffic risk: including Criticality Index Function (CIF), Potential Index for Collision with Urgent Deceleration (PICUD), and Deceleration Rate to Avoid a Crash (DRAC). With increasing MPR, collision risk identified by CIF and DRAC shows an increase tendency, while that identified by PICUD has no apparent trend. Furthermore, the platoon strategy is shown to increase the severity of traffic conflicts significantly. Overall, this study provides novel insights into CAVs deployment through the analysis of platoon strategy. 

Accuracy improvement of Car-following (CF) model has attracted much attention in recent years. Although a few studies incorporate deep reinforcement learning (DRL) to describe CF behaviors, proper design of reward function is still an intractable problem. This study improves the deep deterministic policy gradient (DDPG) car-following model with stacked denoising autoencoders (SDAE), and proposes a data-driven reward representation function, which quantifies the implicit interaction between ego vehicle and preceding vehicle in car-following process. The experimental results demonstrate that DDPG-SDAE model has superior ability of imitating driving behavior: (1) validating effectiveness of the reward representation method with low deviation of trajectory; (2) demonstrating generalization ability on two different trajectory datasets (HighD and SPMD); (3) adapting to three traffic scenarios clustered by a dynamic time warping distance based k-medoids method. Compared with Recurrent Neural Networks (RNN) and intelligent driver model (IDM), DDPG-SDAE model shows better performance on the deviation of speed and relative distance. This study demonstrates superiority of a novel reward extraction method fusing SDAE into DDPG algorithm and provides inspiration for developing driving decision-making model. © 2023 Elsevier B.V.

In vehicular communication protocol stacks, received messages may not always be decoded successfully due to the complexity of the decoding functions, the uncertainty of the communication load and the limited computation resources. Even worse, an improper implementation of the protocol stack may cause an unfair data age distribution among all the communicating vehicles (the receiving bias problem). In such cases, some vehicles are almost locked out of the vehicular communication, causing potential safety risk in scenarios such as intersection passing. To our knowledge, this problem has not been systematically studied in the fields of vehicular communication and intelligent transport systems (ITS). This paper analyzes the root of the receiving bias problem and proposes architectural solutions to balance data age distribution. Simulation studies based on commercial devices demonstrate the effectiveness of these solutions. In addition, our system has been successfully applied during the Grand Cooperative Driving Challenge, where complicated scenarios involving platooning maneuvering and intersection coordination were conducted.

A quantitative understanding of dynamic lane-changing interaction patterns is indispensable for improving the decision-making of autonomous vehicles (AVs), especially in mixed traffic with human-driven vehicles. This paper develops a novel framework combining the hidden Markov model (HMM) and graph structure to identify the difference in dynamic interaction patterns between mandatory lane changes (MLC) and discretionary lane changes (DLC). An HMM is developed to separate the interaction patterns considering heterogeneity in lane-changing processes and reveal the temporal properties of these patterns. Conditional mutual information is used to quantify the interaction intensity, and the graph structure is used to characterize the relationship between vehicles. Finally, a case study is conducted to demonstrate the practical value of the proposed framework and validate its effectiveness in predicting lane-changing trajectories. Based on the lane-changing events extracted from a real-world trajectory dataset, the proposed analytical framework is applied to model MLC and DLC under congested traffic with levels of service E and F. The results show that there could be multiple heterogeneous dynamic interaction patterns in a lane-changing process. A comparison of MLC and DLC demonstrates that MLC involves more intense interactions and more frequent transitions of the interaction network structure, while the evolution rules of interaction patterns in DLC do not exhibit a clear trend. The findings in this study are useful for understanding the connectivity structure between vehicles in lane-changing interactions and for designing safe and smooth driving decision-making models for AVs and advanced driver-assistance systems.