Många förare har ett avsökningsbeteende som systematiskt missar vissa potentiella konflikter, vilket indikerar att deras mentala modeller är felaktiga. Exempelvis förekommer att förare inte tittar efter cyklister som kommer bakifrån innan de svänger höger. Vi undersöker därför hur avsökningsbeteende och anpassning till situationen varierar med omgivande trafik, körriktning, trafikregler och icke-körrelaterade uppgifter, för att förbättra förståelsen av varför förare gör systematiska fel då de söker av omgivningen.

En körsimulatorstudie i stadsmiljö med 40 förare har genomförts. Förarna valdes baserat på självskattad körstil (offensiv/defensiv) samt vana av att cykla i stadsmiljö (erfaren/oerfaren cyklist). Deltagarna exponerades flertalet gånger för tre situationer: sväng i korsning med interagerande cyklist, övergångställe med fotgängare, samt interaktion med andra motorfordon. Under försöket har data om hur förarna kör i förhållande till omgivningen och andra trafikanter, förarnas blickbeteende, förarnas arbetsbelastning, samt hur förarna interagerar med en icke-körrelaterad uppgift samlats in. Analyserna pågår i skrivande stund. Resultaten är därför avgränsade till situationen "korsning med interagerande cyklist".

Förarna stannade eller krypkörde innan de körde ut i korsningen i 254 av de 348 passagerna med högersväng och i 331 av 348 passager med vänstersväng. I många fall sökte försökspersonerna efter informationen för tidigt, t ex tittade försökspersonerna höger/vänster i korsningen när sikten fortfarande blockerades av kringliggande byggnader i 20% av passagerna. Beroende på kategori tittar mellan 50–80% av förarna i speglarna och/eller över axeln efter cyklister innan de svänger ut i korsningen. Den kategori som tittar oftast är de defensiva förarna som även är vana cyklister.

Högersvängsolyckor är ett vanligt scenario där cyklister blir påkörda. En trolig orsak är att förare inte söker av sina döda vinklar. Frågan är varför förare glömmer, eller inte ens känner till, att de behöver titta efter cyklister som kommer bakifrån. Våra preliminära resultat ger inga tydliga svar, mer än att bilister som även har erfarenhet av att cykla i stadstrafik är bättre på att samspela med cyklister.

In most recent cases, motivated by the need to conduct tests in both physical and virtual test environment, a digital twin of the physical world is often created in the virtual environment to be used for testing purposes. This approach is often used in when testing automated driving systems (ADS) which are being extensively tested by companies and cities around the world. While this approach may work well with ADS, whose behaviour can be deterministic in both physical and virtual worlds, human drivers are known to exhibit different behaviour in simulation compared to real situations.

Since human drivers are still expected to be involved in future transport systems, the difference in driving behaviour needs to be understood in order to assist in verification and validation of the transport system. Therefore, in this work, we intend to investigate the differences in driving behaviour of human drivers, comparing the same manoeuvre done on the proving ground to the manoeuvre recorded in a driving simulator. 

Data collected from a proving ground will be compared with data collected from a driving simulator (under the same manoeuvre) to analyse differences in driving behaviour between the two test environments. More specifically, there are two datasets in this work: 1) data collected from AstaZero proving ground; and 2) data collected from the "Sim IV" driving simulator at VTI.

The difference between collected data will be analysed using statistical methods. The results indicating difference in driving behaviour between the two datasets will be presented. The results will be analysed and presented in the context of verification and validation of transport systems using digital twins.

This work intends to present the differences in driving behaviour of human drivers, comparing the same manoeuvre done on the proving ground to the manoeuvre recorded in a driving simulator. The result will provide some indication on the validity of using driving simulator as a virtual test environment for verification and validation of transport systems.

Automotive innovations are increasingly software-driven, necessitating frequent updates. Current validation processes heavily rely on physical testing, which is time-consuming and costly. The project focuses on how vehicle functionalities could be tested and validated in simulation models and what fidelity level that could be reached. By utilizing virtual environments, the project aims to proactively test software functions before deployment, ensuring accurate assessments of system performance in diverse scenarios.

The primary goal is to develop strategies that balance the realism of virtual test environments with practical implementation. Key research questions include:

• What level of realism is required for simulations to be credible for testing edge cases?
• How can virtual testing be integrated with real-world data to discover new edge cases?
• How can virtual testing ensure functional safety to satisfy regulatory bodies?

The project also seeks to establish metrics for comparing physical and virtual test results and to utilize open-source tools for broader industry use.

The project identified gaps between physical and simulated test results, such as differences in braking activations between physical test and simulation. It also highlighted the need for improving simulation tools' ability to replicate real-world vehicle behaviour accurately.

Key findings include:

• Virtual tests can be reliable but require tuning to achieve higher fidelity.
• Physical tests remain crucial for validating simulation models.
• Establishing standardized KPIs for virtual testing is essential to enhance credibility.

The project faced several challenges such as:

• Variability in sensor models across partners.
• Human factors introducing inconsistencies in physical tests.
• Limitations of existing simulation tools to accurately replicate real-world scenarios.

A comprehensive list of challenges was compiled to guide future research and development efforts.

EVIDENT successfully demonstrated the potential of virtual validation for ADAS and AD features. The project contributed to developing methodologies for comparing physical and virtual tests and provided insights into the requirements for credible virtual toolchains.

På VTI har tre simulatorstudier genomförts under 2024 som använder sig av denna teknik. I dessa tre studier har utrustningen använts som körsimulator, simulator för gångare och simulator för cyklister. Vid byggandet av simulatorlösningarna så provades ett antal olika arbetssätt och inställningar för att sedan landa i hur studierna genomfördes. När studierna genomfördes så tillfrågades deltagarna i slutet av studierna om hur de tyckte det gick att använda utrustningen och om hur realistisk deras upplevelse var. Dessa svar har sedan analyserats och jämförts med historiska data från tidigare simulatorstudier.

Vid användning av ”green-screen” teknik för XR så har det varit viktigt med bra ljusförhållanden. För gångare användes extra ”trackers” för att ge ett tillfredsställande rörelsebeteende. Dessa ”trackers” behöver vara i kontakt med basstationer så med väldigt rörliga gångare kan fler basstationer behövas. XR systemen kräver mycket datorprestanda så det är viktigt att ha rätt hårdvara och en del optimering kan behövas för att få tillfredsställande prestanda. Vid långvarig användning så kan det vara bra att stänga av/starta om systemet då och då. Försökspersoner som har varit gångare eller cyklister beskriver en högre realism än förare även om alla tycker att det har varit realistiskt. Det fanns heller inga indikationer på en ökad simulatorsjuka och för cyklister så rapporterades den lägsta simulatorsjukan. De flesta tyckte att det gick bra att använda ett headset och tyckte inte att det var speciellt besvärande. Att kunna se detaljer i miljön verkar ligga på samma nivå som med skärmar.

Från dessa studier så kan vi se att huvudbaserade XR/VR lösningar ger ytterligare möjligheter till simulatorstudier och kompletterar på så sätt skärmbaserade simulatorer. En fördel är att de ger en friare känsla av att röra sig och kunna titta runt samtidigt som de kan spåra var försökspersoner tittar. En nackdel är att de är prestandakrävande vilket ger en utmaning i att skapa väldigt levande miljöer. Sammanfattningsvis så kommer VTI fortsatt att använda tekniken.

Ports are striving to improve operational efficiency in the context of constantly growing volumes of trade. In this context, port terminal storage yard operation is key, since complexity and poor coordination lead to containers stacked without consideration of retrieval schedules, resulting in time and energy-consuming reshuffling operations. This problem, known as the block relocation (and retrieval) problem (BRP), has recently gained considerable attention. Indeed, there are promising solutions to the BRP. However, the literature views the problem in isolation, optimizing one operational parameter for one of the many port stakeholders. This often leads to efficiency losses since port processes involve different stakeholders and port parts. In this work, we explicitly focus on scheduling trucks for pick-up for hinterland distribution. Appointments are often postponed in order to minimize reshuffling operations, leading to losses for the transport forwarders and decreasing the competitiveness of the port.

We discuss the trade-off between minimizing container reshuffling operations while maintaining scheduled time windows for container retrieval. We describe the multi-objective optimization problem as a weighted sum of the two objectives. Given the complexity of the problem, we also present a greedy heuristic. Our results indicate that the number of schedule deviations can be reduced without significantly affecting the number of relocations compared to solutions that consider only the latter. Ideally, a weighting of 0.4 and 0.6 should be applied, reflecting schedule deviations and relocations, respectively, to achieve the highest joint optimization potential. This demonstrates that in complex environments, such as ports, with multiple interacting stakeholders and processes, coordination of solutions yields significant benefits.

The DiREC CAV-Readiness Framework (CRF) is a framework that aims to help an NRA assess their capability with respect to the deployment of Connected and Automated Driving (CAD) solutions, their ability to influence the use of CAD on their network via investment in digital and physical infrastructure, and the impacts and outcomes of any investment decision.

DiREC structured the CRF around C-ITS Services and Use Cases as defined under the C-ROADS project. C-ROADS is a joint initiative of European Member States and road operators for testing and implementing C-ITS services, with a desire for cross-border harmonisation and interoperability.The CRF is thus a framework which can be used by NRAs to help assess their aspirations and readiness to support CAD, and to implement individual C-ITS services and use cases. It does this by:

• Defining the C-ITS services to be provided
• Breaking those services down into use cases and enablers
• Scoring the NRAs readiness, aspirations and high-level assessment of costs and impacts of each enabler to help plan and prioritise the NRA support for CAD.

The 2020 CEDR Research Call on the Impact of CAD on Safe Smart Roads had as its aim to “prepare the national road authorities on future challenges of connectivity, digitalization and automation to get to an autonomously well-managed traffic flow.” CEDR cautioned that “ If NRAs do not act proactively, the vehicle manufacturers will determine the automation of traffic flow alone, the NRAs will fall behind and huge investment will be needed to safeguard NRAs’ objectives. NRAs’ goals and roles in the Cooperative, Connected and Automated Mobility of the future must be clear…NRAs need to determine and act before other parties decide in our place where we need to invest.”.

The Digital Road for Evolving Connected and Automated Driving (DiREC) project set out to develop a toolkit for NRAs to use in their assessment and development of their capabilities as digital road operators. This toolkit, referred to as the CAV Readiness Framework (CRF), focuses on five key areasof provision that are central to the development of connected and automated driving capabilities. Those areas being:

• Physical infrastructure
• Digital infrastructure
• Communications infrastructure
• Standards and regulation
• Operational support

DiREC focused on technologies, services and regulatory infrastructure for which NRAs might either have direct responsibility or at least significant influence. Development led by vehicle manufacturers or the regulations governing them were considered to be outside the scope of influence of the NRAs, for the moment at least. Instead, attention was given to those technologies and services that are likely to be generically useful to most or all future CAD solutions. We identified that there is still a considerable diversity of approaches amongst the developers of automated vehicles, with no single technical strategy yet close to being dominant.

The Level of Service (LoS) is a widely employed metric that quantifies the performance and quality of a provided service, utilizing a predetermined scale. In the transportation domain, road capacity (i.e., maximum throughput in a given road section) is the most widely used performance indicator, where the LoS is applied. In road capacity studies, the LoS definition is dependent upon the specific context and facility under examination, such as urban areas or motorways. In urban settings, the criteria typically employed for determining LoS include average travel speed, average travel time, frequency of stops, and delays. Conversely, on motorways, LoS is determined by factors such as vehicle density, traffic speed, and frequency of lane changes (HCM, 2016). Upon specifying the context, the chosen criteria are applied, and threshold requirements are established to categorize the performance and quality under the appropriate LoS. The LoS scale can range from binary levels (e.g., acceptable or unacceptable) to more nuanced scales. For instance, the HCM (2016) employs a six-point scale (A = very good; B = good; C = acceptable; D = bad; E = very bad; F = system breakdown).

For C-ITS services (i.e., information provision), however, the aim is to provide information that are, among other things, accurate and timely to the road users, connected and autonomous vehicles, so they can react accordingly to events on the road network. The CAV-ready framework (CRF) developed in WP3 aims to illustrate the progress of National Road Authorities (NRAs) towards becoming a digital authority, meaning that the NRAs should provide traffic related information (data provision) to its users (connected and autonomous vehicles) that are precise, accurate, and timely. As such, we have defined three distinct LoS categories:

1. Basic: Minimum acceptable performance/quality
2. Enhanced: Not optimal but sufficient performance/quality
3. Advanced: Ideal or best performance/quality

Connected and Automated Driving (CAD) is an important area of digital technology that will bring disruption to individuals, economies, and societies. Most forms of CAD require some level of infrastructure support for their safe operation. Additional infrastructure and services to support CAD have the potential to improve safety even further, and to bring other benefits such as increased efficiency or reduced congestion. However, the infrastructure requirements from Original Equipment Manufacturer (OEMs) are not always clear, and it is difficult for National Road Authorities (NRAs) to predict and plan for the future levels of support needed for CAD given rapidly evolving technology and uncertain projections of future CAD demand. In addition, there is also a need for better dialogue among NRAs, OEMs and service providers to articulate those requirements and to define a roadmap and responsibilities for achieving safe and smart roads through CAD.

The aim of DiREC is to establish a CAV Readiness Framework and a set of toolkits dedicated to CAVs (Connected and Autonomous Vehicles) that incorporates a wide range of components that affect CAD and the ability of highway infrastructure to support it. These components include machine readability of physical infrastructure, digital services, connectivity, in addition to aspects such as governance of the infrastructure and services, and legal and regulatory requirements. Together these components influence the ability of the NRA to become a digital road operator. The DiREC project will thus provide a framework for NRAs, service providers and OEMs to support CAD. It will consolidate and combine standards, research, and recommendations from other projects and extend research into new areas such as creating a common vision for digital twins among NRAs, understanding connectivity and connectivity requirements to support digital services and analysing how these can be met, reviewing the quality management processes around digital data, and documenting existing legal and regulatory frameworks in all areas relating to CAD.

The main aim of the DiREC project is developing a common framework to support National Road Authorities (NRAs) to provide better engagement with Original Equipment Manufacturers (OEM) and service providers, identify clearer responsibilities and liabilities, and include tools to calculate the costs and benefits of providing different levels of support to Connected and Automated Vehicles (CAVs). Greater engagement and dialogue are key. By understanding the infrastructure and communications requirements of automated vehicles, and the challenges faced by CAVs in an operational environment, NRAs will be able to strategically plan their networks to support Connected and Automated Driving (CAD) and place themselves in a much stronger position to influence how traffic operates on the network.

A proactive approach to liaising with vehicle manufacturers and service providers will also promote NRA involvement in the services that are developing around digital mapping, localisation, navigation and traffic management. By aligning the digital strategies and plans of the NRAs with the requirements of OEMs and CAVs, and by giving direction to service providers, a common framework for CAD will help achieve major cost efficiencies and facilitate economic transformation. It will help optimise the delivery of infrastructure and communications systems on national road networks in support of CAD implementation whilst helping NRAs maintain their influence over CAD activity. In order to facilitate productive interactions with various stakeholders involved with CAVs, WP1 within the DiREC project is dedicated to stakeholder engagement activities. This will ensure that input from different stakeholder categories will be collected and utilized for CAV-ready framework development.