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SUMO 2017 Towards Simulation for Autonomous Mobility
Abstract and Figures
This volume contains the proceedings of the SUMO Conference 2017 which was held from 8th to 10th May 2017 with a focus on autonomous mobility. In the current transition process traffic simulation is the only tool which can give us insights in the mechanisms of traffic in largely automatized traffic scenarios. SUMO as an open source tool provides a wide range of traffic planning and simulation functionalities to support the scientific community. The conference proceedings offer an overview of the applicability of the SUMO tool suite as well as its universal extensibility due to the availability of the source code. The major topic of this fifth edition of the SUMO conference is the calibration of simulation to real world or handbook data as well as communicating networks of intelligent vehicles. A number of contributions cover heterogeneous traffic networks, junction control and new traffic model extensions to the simulation. Subsequent specialized issues such as emission modelling and personal rapid transit simulation are targeted as well. At the conference the international user community exchanged their experiences in using SUMO. With this volume we provide an insight to these experiences as inspiration for further projects with the SUMO suite.
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... bus lanes), the slope of the road, the number of circulating vehicles per hour and vehicle type. It should be mentioned that the validity of the microsimulation traffic model SUMO used in the current study has been investigated in the past through the comparison of the simulated vehicle speeds with measurement data [19,34]. ...
This study aims to investigate, in microscale, the pollutant emissions due to road traffic under traffic-congested conditions at street level and the impacts on air quality of traffic emissions reduction scenarios by applying an Integrated Modelling Tool (IMT) for a main road axis in Thessaloniki, Greece. ΙΜΤ links a real-world traffic model, a dynamic emissions model and a Lagrangian dispersion model coupled with a boundary layer flow module. Pollutant emissions from cars at edges with traffic lights were + 30% higher than those estimated at other edges while NOx emissions were + 22% higher at the edges with bus stops. A comparison of the IMT and COPERT Street Level emissions results showed that the IMT emissions were higher than the COPERT Street Level emissions for roads with traffic lights or bus stops, characterized by high variability in vehicle speed per second due to stopping and accelerating. This resulted in up to 2 times higher NOx emissions. IMT was applied to assess the impact on the atmospheric environment of the redesign of the road axis promoting sustainable urban transportation. A reduction by − 20% of the cars and motorcycles traffic flows in combination with the increase by a factor of 2 of the frequency in the circulation of city buses replaced with battery electric vehicles will result in lower pollutant and CO2 emissions ranging from − 29 to − 41%. Reductions of about − 65% in the road traffic NOx maximum concentration levels were also estimated.
... Although SUMO was not originally designed to address autonomous vehicles, it is a very flexible tool which can include different driver models. In fact, SUMO has been recently updated to model autonomous vehicles, and there are several scientific publications that use this mobility simulator for that purpose [32,33]. ...
Currently, one of the main challenges that large metropolitan areas must face is traffic congestion. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, environmental pollution. By properly analyzing traffic demand, it is possible to predict future traffic conditions, using this information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution, thereby improving the traffic flow in a city in a fully centralized manner. This paper represents a step forward towards this novel traffic management paradigm by proposing a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. We perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed traffic prediction equation, combined with frequent updating of traffic conditions on the route server, can achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity.
The active development of Automated Guided Vehicle Systems (AGVSs) has started during the 70th. These systems, often called Personal Rapid Transit (PRT), are fully automated and move 3-6 passengers in electric vehicles on small-size guide-ways with just 1 square meter intersection. The network can operate 24h a day with a small workforce and offers vehicles on-demand. Each trip is non-stop, origin-to-destination without intermediate stops or transfers. Despite the numerous advantages that derive directly from these properties, there is no AGVS in public operation today. The reasons are manifold, but AGVSs constitute, without any doubt, an enormous technical challenge because: (i) The time headway between two consecutive vehicles must be extremely short (approximately one second), in order to guarantee a sufficiently high line capacity. (ii) The number of vehicles that need to be simultaneously coordinated is huge (several thousands).
In this paper we want to review AGVS network control alternatives, discuss their main advantages and drawbacks and suggest a different predictive method, which appears to be most appropriate to simplify the above mentioned problems. In particular it has the potential to avoid vehicle congestions, while keeping the local vehicle-distance control separated from the global logistic control.
It is time consuming to set up a realistic traffic simulation scenario. Even though data is available, a large effort is needed to gather, convert and adapt all the data needed to replicate a part of a real road network. Available road networks usually have to be corrected and adapted to the used simulation model. The demand has to be converted or even generated using given measurements. The measurements must be imported into the simulation system’s architecture to allow the models’ calibration and validation. Additional road side structures must be converted into a proper representation and embedded into the scenario. Therefore, three real world traffic simulation scenarios of the city of Bologna are made available to the public. With these scenarios researchers are able to start their investigations with little preparation effort and can concentrate on their research questions.
Here we describe the development of the London Hybrid Exposure Model (LHEM), which calculates exposure of the Greater London population to outdoor air pollution sources, in-buildings, in-vehicles and outdoors, using survey data of when and where people spend their time. For comparison and to estimate exposure misclassification we compared Londoners LHEM exposure with exposure at the residential address, a commonly used exposure metric in epidemiological research. In 2011, the mean annual LHEM exposure to outdoor sources was estimated to be 37÷ lower for PM2.5 and 63÷ lower for NO2 than at the residential address. These decreased estimates reflect, the effects of reduced exposure indoors, the amount of time spent indoors (~95÷), and the mode and duration of travel in London. We find that an individual’s exposure to PM2.5 and NO2 outside their residential address is highly correlated (Pearson’s R of 0.9). In contrast, LHEM exposure estimates for PM2.5 and NO2 suggest that the degree of correlation is influenced by their exposure in different transport modes. Further development of the LHEM has the potential to increase the understanding of exposure error and bias in time-series and cohort studies, and thus better distinguish the independent effects of NO2 and PM2.5.
This paper describes a new simulation framework assisting the development of ADAS optimized for Chinese traffic. The framework couples the traffic simulators SUMO and VTD to combine different scales and fidelities. SUMO focuses on traditional European and American traffic, making it necessary to implement, calibrate and validate new traffic models. To this end, two driver studies conducted in China are described first. It is stated which characteristics in the driving behavior can be found and how they are reproduced in the simulation. Subsequently, the development and the implementation of the simulation framework are presented and test results are discussed. The focus on Chinese traffic demands a sophisticated combination of classic microscopic traffic simulations and more detailed considerations of the ego vehicle surroundings which are both integrated in the presented framework.
One essential part of a working health system is that people who are seriously injured or sick receive professional assistance as fast as possible. Therefore there is an optimal rescue time for emergency vehicles (in Germany the rescue time is normally around 8 - 15 minutes). To keep this rescue time, emergency vehicles like fire trucks, ambulances and police cars have special rights in case of an emergency situation. These special rights give emergency vehicles priority at intersections and allow them to violate red lights etc. But driving an emergency vehicle is still a dangerous and stressful situation. The probability of having an accident with an emergency vehicle that is driving with siren and flashing blue light is much higher than with a normal car and the probability that someone will be killed in an accident is also 4 times higher. This paper analyses the reasons for accidents with emergency vehicles and presents a description of the driving behavior for emergency vehicle. Furthermore typical accident situation are determined. The evaluation showed that most accidents happen at controlled and uncontrolled intersections. Mostly, not the traffic behavior of emergency vehicle is the only influence on traffic safety; also other traffic participants play an important role.
This public deliverable is an extension of the draft deliverable D4.1. The first part covers the extensions performed on PHEM’s database for modelling the vehicle fleet in the year 2020. The document extension describes the second work item which was to allow using PHEM as an emission model directly within the COLOMBO overall simulation system (COSS). Both possibilities – an off-line connection with SUMO output files fed into PHEM, and an on-line approach by embedding the derivative PHEMlight into SUMO – are presented in detail.
Environment perception is one of the most important tasks for Advanced Driver Assistance Systems and Automated Driving. Various types of sensors are used concurrently and fused into a consistent representation of the vehicle's surrounding. With more than one device involved, timing behavior becomes a relevant aspect of such systems. Delays due to processing and transferring data need to be considered when fusing sensor data.
While some sensors, such as camera devices, perceive the environment as a snapshot, other sensors are based on a scanning technique. The iterative acquisition principle of scanning sensors lead to considerable time differences between single measurements within a scan. In addition to transfer and processing delays, these time differences have to be considered properly for a comprehensive environment representation.
This paper focuses on time-related effects resulting from using a scanning sensor. The influence on two commonly utilized environment representations is discussed, grid-based and object-based approaches for stationary and movable elements, respectively. We present a comprehensive approach to incorporate the ego motion into grid-based models. We propose an adaptive prediction horizon for object tracking algorithms, based on sensor scan timing characteristics. Performance gains are evaluated with simulated data and verified within a real-world application.
The presented algorithms are computationally feasible and real-time capable on a standard PC.
Future advanced driver assistant systems and automated driving put high demands on the environmental perception especially in urban environments. Major tasks are the perception of static and dynamic elements along with the drivable area and road structures. Although these tasks can be done without an explicit representation of the ground surface, evaluations with real-world sensor data have shown that many failures in sensor data interpretation result from inappropriate ground point filtering, e.g. false negative or false positive detection of objects. That means, the ground plane estimation becomes a key-task in urban environment modeling assuming a reliable perception of the local vehicle's surroundings is required. In this paper we thus present the enhancements of the environment modeling that can be derived from an explicit representation of the ground surface. We propose the combination of channel-wise ground classification with a grid-based representation of the ground height to cope with spatial false measurements and exploit interchannel dependency. Partial occlusions by other road users are handled in an efficient way. Integration of the ground data is shown by a pitch angle estimation and a curb detection module. The algorithms run in real-time on a standard PC and are evaluated with real sensor data.
Automated driving is a widely discussed topic nowadays. Impressive demonstrations have shown the potentials of vehicle automation. However, many projects in the context of automated driving use a priori data in order to compensate insufficiencies in perceiving and understanding the vehicle's environment. Additionally, in terms of functional safety and redundancy, it is not yet known whether such localization-and map-based approaches are really path breaking. This is the reason why we focus on on-board perception also of the stationary urban environment. While object tracking is a commonly used approach, the combination of grid-based and object-based representations for environment perception is still a research topic. The sufficient perception of lanes and drivable areas is an unsolved issue in urban environment. Several perception modules have to collaborate for a suitable representation of the vehicles' surroundings. In this paper, we present the latest contributions of the project Stadtpilot to a perception-driven modeling of urban environments. We propose a lane detection approach which is based on a grid-based representation of different environmental features. Our approach is able to detect multi-lane structures and it is capable to deal with complex lane structures which are typical of urban roads. The extracted features are stabilized by a tracking module. Additionally, we incorporate a free-space representation which data is not derived implicitly from detected targets, but based on an explicit ground representation. Extensions of our dynamic classification module focus on the start/stop behavior of other road users in order to enhance the completeness of track list (mobile objects) and grid (stationary environment). The presented algorithms run in real-time on a standard PC and are evaluated with real sensor data.