Platooning will soon likely to be common on Swedish roads and the potential for fuel  savings in the transport sector is high. This pre-study project explores the need for  external signaling in platoons to avoid any cut-ins from surrounding vehicles whose   drivers are unaware that their actions may cause a loss of fuel saving. Interviews with  truck drivers created an understanding of how they experience the behavior of the  surrounding traffic. The scenarios that are highlighted where unaware cut-ins may occur  are mainly on-ramps and while overtaking on highway. Car drivers highlighted that overtaking may be a problem, especially on 2+1 roads. Communication needs elicited in workshops with drivers mainly concerned the movement patterns and properties of vehicles, e.g. speed, direction, gaps and length of the platoon. Barriers that were identified for external signaling is that trailers are constantly rotating between different  tractors. This may require that more trailers than tractors need to be equipped with  communication devices. To evaluate the potential impact of external signaling simulation  could be used, where a driving simulator could be used to evaluate the perception of car- and truck drivers. Different means of communication, behavior, driving close together or lighting could be subject to evaluation. The long-term learning effect and behavioral adaptation to platooning in traffic is also important to study. It was found that there are large regional behavioral differences in traffic. Naturalistic data from the  US, indicate that there are no cut-ins if the distance between trucks is < 30 m. In Europe, the data collected from ETPC indicate that there is up to one cut-in every 15 km on highways. The data from the ETPC is however very sparse compared to the US study. In Sweden, it does not seem to be a specific need for external signaling since very few cut-ins occur. In Europe, more cut-ins occur and external signaling could help to save fuel. It is however unclear what long-term effects external signaling may have. Further studies are suggested to study if short platooning distance (10-20 meters) is sufficient to deter surrounding traffic from cut-ins.

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.

Large amounts of data and information is generated for different purposes every day in the traffic system. There is an increased interest within the Swedish Transport Administration (STA) in using this data for planning of maintenance, traffic management and strategy work. In this report the first steps towards such a system is developed by the process of defining a business objective, collecting data, understand the data, prepare the data, create a model and evaluate the results. All these different steps were important in the performed study. To find a good case creating business value required lots of discussions and interviews with key figures at STA. The case investigated is to predict the traffic situation on four road segments in Gothenburg based on two years of data for the traffic situation, weather, and road situation including accidents and road works.

The data for primarily weather and traffic are not collected for the purpose of being used for this application. This is one reason for that data is missing from some of the data sets for different time periods. One conclusion from the project is that data analysts must be included not only in the data analyze phase, but also in the data collection phase to achieve good results.

Different methods for creating data driven models are evaluated and compared based on the two year period of data available. It is found that linear regression performs better than tree-based classification and prediction method regarding performance, while the tree-based method more clearly can create understanding for what variables that correlate to the traffic situation.

The methods for developing models based on the data used in this project are generic and are possible to be used when larger data sets are available. Additional data sources, such as events in the city and building works may also be included in such analysis. Furthermore, it is found valuable to have the possibility to develop models on a local computer based on a smaller data set, and make the final computations based on the larger data sets in a cloud based solution.

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.

Free-floating car sharing is a form of car rental used by people for short periods of time where the cars canbe picked up and returned anywhere within a given area. In this paper, we have collected free-floating carsharing data, for electric as well as fossil fueled cars, and data for alternative trips using cycling, walking andpublic transport for the cities of Amsterdam, Berlin, Denver, Madrid, San Diego and Seattle. From this data,free-floating car sharing seems to be a compliment to other type of transports, including public transports,bicycling and walking, and not a competitor.

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.

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.

Free-floating car sharing is a form of car rental used by people for short periods of time where the cars can be picked up and returned anywhere within a given area. In this paper, we have collected free-floating car sharing data, for electric as well as fossil fueled cars, and data regarding e.g. size of the city, number of cars in the service, etc. The utilization rates of the free-floating car sharing services vary much between the cities, greatly influencing the success of the services. This paper presents the most important factors influencing the utilization rate, and also a methodology to predict the utilization rate for new cities, using data mining based on Random Forests.

This paper presents a novel approach to modelling visual distraction of bicyclists. A unique bicycle simulator equipped with sensors capable of capturing the behaviour of the bicyclist is presented. While cycling two similar scenario routes, once while simultaneously interacting with an electronic device and once without any electronic device, statistics of the measured speed, head movements, steering angle and bicycle road position along with questionnaire data are captured. These variables are used to model the self-assessed distraction level of the bicyclist. Data mining techniques based on random forests, support vector machines and neural networks are evaluated for the modelling task. Out of the total 71 measured variables a variable selection procedure based on random forests is able to select a fraction of those and consequently improving the modelling performance. By combining the random forest-based variable selection and support vector machine-based modelling technique the best overall performance is achieved. The method shows that with a few observable variables it is possible to use machine learning to model, and thus predict, the distraction level of a bicyclist.

In recent years, free-floating car sharing services (FFCS) have been offered by many organizations as a moreflexible option compared to traditional car sharing. FFCS allows users to pick up and return cars anywherewithin a specified area of a city. FFCS can provide a high degree of utilization of vehicles and less usage ofinfrastructure in the form of parking lots and roads and thus has the potential to increase the efficiency of thetransport sector. However, there is also a concern that these compete with other efficient modes of transport suchas biking and public transport. The aim of this paper is to better understand how, when and where the vehiclesare utilized through logged data of the vehicles movements. We have access to data collected on FFCS servicesin 22 cities in Europe and North America which allows us to compare the usage pattern in different cities andexamine whether or not there are similar trends. In this paper, we use the collected data to compare the differentcities based on utilization rate, length of trip and time of day that the trip is made. We find that the vehicleutilization rates differ between cities with Madrid and Hamburg having some of the highest utilization levels forthe FFCS vehicles. The result form a first step of a better understanding on how these services are being usedand can provide valuable input to local policy makers as well as future studies such as simulation models.

Free-floating car sharing services (FFCS) have been offered as a more flexible mobility solution than other car sharing services. FFCS users can pick up and return cars anywhere within a specified area in a city.The objective of this paper is to identify similar usage patterns of FFCS in different cities as well as city characteristics that make these services a viable option. The authors have access to real booking data for 32 cities in Europe and North America. Their study shows the share of daily car trips is negatively correlated to the utilization rate of these services. Also, the higher the congestion and the harder finding a parking lot, the lower the utilization rate of these services is in the cities. Moreover, our results suggest that FFCS services do not compete with public transport but are rather used in combination to it. These services are mainly used during midday and evening peak and the trips taken by these services are mainly chained trips.The clustering analysis shows that the trips are grouped into two or three clusters in different cities. The majority of clusters are the inner city clusters which contain a significantly higher number of trips than the clusters around other points of interest such as airports.

Cooperative driving is based on wireless communications between vehicles and between vehicles and roadside infrastructure, aiming for increased traffic flow and traffic safety, while decreasing fuel consumption and emissions. To support and accelerate the introduction of cooperative vehicles in everyday traffic, in 2011, nine international teams joined the Grand Cooperative Driving Challenge (GCDC). The challenge was to perform platooning, in which vehicles drive in road trains with short intervehicle distances. The results were reported in a Special Issue of IEEE Transactions on Intelligent Transportation Systems, published in September 2012 [item 1 in the Appendix].

Cooperative adaptive cruise control and platooning are well- known applications in the field of cooperative automated driving. However, extension toward maneuvering is desired to accommodate common highway maneuvers, such as merging, and to enable urban applications. To this end, a layered control architecture is adopted. In this architecture, the tactical layer hosts the interaction protocols, describing the wireless information exchange to initiate the vehicle maneuvers, supported by a novel wireless message set, whereas the operational layer involves the vehicle controllers to realize the desired maneuvers. This hierarchical approach was the basis for the Grand Cooperative Driving Challenge (GCDC), which was held in May 2016 in The Netherlands. The GCDC provided the opportunity for participating teams to cooperatively execute a highway lane-reduction scenario and an urban intersection-crossing scenario. The GCDC was set up as a competition and, hence, also involving assessment of the teams' individual performance in a cooperative setting. As a result, the hierarchical architecture proved to be a viable approach, whereas the GCDC appeared to be an effective instrument to advance the field of cooperative automated driving.

In recent years, free-floating car sharing (FFCS) services have been offered as a more flexible option compared to traditional car sharing. FFCS allows users to pick up and return cars anywhere within a specified area of a city. These can be either electric or fossil driven vehicles. We analyze the difference in usage of these two types of vehicles. The analysis is based on a dataset consisting of vehicle availability data sampled between 2014 and 2016 for 9 cities with EVs in the FFCS fleet. We find that there is no statistical difference in how EVs and fossil driven FFCS vehicles are used. When it comes to charging of EVs two main strategies are identified: widespread “slow charging” versus tailored fast-charging.

Free-floating car-sharing (FFCS) allows users to book a vehicle through their phone, use it and return it anywhere within a designated area in the city. FFCS has the potential to contribute to a transition to low-carbon mobility if the vehicles are electric, and if the usage does not displace active travel or public transport use. The aim of this paper is to study what travel time and usage patterns of the vehicles among the early adopters of the service reveal about these two issues. We base our analysis on a dataset containing rentals from 2014 to 2017, for 12 cities in Europe and the United States. For seven of these cities, we have collected travel times for equivalent trips with walking, biking, public transport and private car. FFCS services are mainly used for shorter trips with a median rental time of 27 min and actual driving time closer to 15 min. When comparing FFCS with other transport modes, we find that rental times are generally shorter than the equivalent walking time but longer than cycling. For public transport, the picture is mixed: for some trips there is no major time gain from taking FFCS, for others it could be up to 30 min. For electric FFCS vehicles rental time is shorter and the number of rentals per car and day are slightly fewer compared to conventional vehicles. Still, evidence from cities with an only electric fleet show that these services can be electrified and reach high levels of utilization.

Long haulage trucks consume large amounts of fuel, and fuel savings are desired both from economical and environmental aspects. When the upcoming road topology is known, the speed and gear shifts can be optimized in order to minimize the fuel consumption by e.g. minimizing the braking of the truck. Three different optimal control approaches are evaluated and compared for the speed and gear shift optimization problem. The results are based on simulations, but two of the three evaluated solvers are also implemented on-board a truck using rapid prototyping to investigate the feasibility of such systems. The results indicate that optimal control of the speed reduces the fuel consumption more than finding the optimal gear shift trajectory. The overall optimization problem contains one discrete and one continuous state, which makes the selection of optimization method complex. A sequential optimization scheme where the optimal speed profile is found using linear programming and the optimal gear profile is found using dynamic programming shows similar results as using dynamic programming for the overall problem simultaneously. One drawback with this solution is robustness and several tuning parameters. The driveability of the solutions are found good at the performed on-board tests. 

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. 

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. 

Dependable and fail-safe control of autonomous vehicles requires multiple independent sensors for lane detection and positioning. From analysis of modern sensing technologies, we conclude that radars are underutilized for positioning, and that they might be an enabling technology for achieving safety requirements posed by the standard ISO 26262. To fully utilize the radar potential, we have conducted a pre-study of equipping infrastructure with radar reflectors. We estimate that such reflectors should be installed in the lane markings, about 20-25 meters apart and with some kind of identification. We propose to design and evaluate a combi-reflector based on the traditional cat’s eye design, which will be detectable both by human drivers, radars and lidars. Furthermore, the combi-reflector can be equipped with a magnet for in-vehicle electromagnetic field sensor. From the redundancy evaluation performed, we conclude that the proposed solution increases the level of redundancy significantly. Therefore, the proposed solution could be an enabler for autonomous driving.