Pablo Alvarez Lopez

Bio: Pablo Alvarez Lopez is an academic researcher from Braunschweig University of Technology. The author has contributed to research in topics: Traffic simulation & Inductive charging. The author has an hindex of 3, co-authored 8 publications receiving 678 citations.

Microscopic Traffic Simulation using SUMO

Abstract: Microscopic traffic simulation is an invaluable tool for traffic research. In recent years, both the scope of research and the capabilities of the tools have been extended considerably. This article presents the latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO.

Implementation of an Energy Model and a Charging Infrastructure in SUMO

Abstract: Future traffic that will be accompanied by higher alternative drive concepts will pose as a challenge when it comes to corresponding energy systems, coordination of operations, and communication interfaces, such as needed for data acquisition and billing. On one hand, the increasing attractiveness of electric vehicles will inevitably lead to the development and testing of compatible technologies; on the other, these will need to be conformed to existing systems, when integrating them into the prevailing infrastructure and traffic. Funded by the German Federal Ministry of Transport, Building and Urban Development, an inductive vehicle charging system and a compatible prototype bus fleet shall be integrated into Braunschweig’s traffic infrastructure in the scope of the project emil (Elektromobilitat mittels induktiver Ladung – electric mobility via inductive charging). This paper describes the functional implementations in SUMO that are required by the methodic approach for the evaluation of novel charging infrastructures by means of traffic simulation.

Implementation of an Energy Model and a Charging Infrastructure in SUMO.

Abstract: Future traffic that will be accompanied by higher alternative drive concepts will pose as a challenge when it comes to corresponding energy systems, coordination of operations, and communication interfaces, such as needed for data acquisition and billing. On one hand, the increasing attractiveness of electric vehicles will inevitably lead to the development and testing of compatible technologies; on the other, these will need to be conformed to existing systems, when integrating them into the prevailing infrastructure and traffic. Funded by the German Federal Ministry of Transport, Building and Urban Development, an inductive vehicle charging system and a compatible prototype bus fleet shall be integrated into Braunschweig’s traffic infrastructure in the scope of the project emil (Elektromobilitat mittels induktiver Ladung – electric mobility via inductive charging). This paper describes the functional implementations in SUMO that are required by the methodic approach for the evaluation of novel charging infrastructures by means of traffic simulation.

Supporting additional elements in a simulation using Netedit

Abstract: The traffic simulation software SUMO supports the simulation of various kinds of network infrastructure such as bus stops, traffic detectors and variable speed signs This network infrastructure is configured with various parameters in regard to its location and functionality So far, the definition and customization of these infrastructure objects required writing custom XML files which is a tedious and error-prone endeavor To simplify this task the visual network editor Netedit is being extend to support the creation and customization of infrastructure objects The GUI facilities should provide for an intuitive user experience and the architecture is designed with user-defined infrastructure types in mind

SUMO2015 - Intermodal Simulation for Intermodal Transport

Abstract: Dear reader, You are holding in your hands a volume of the series „Reports of the DLR-Institute of Transportation Systems“. We are publishing in this series fascinating, scientific topics from the Institute of Transportation Systems of the German Aerospace Center (Deutsches Zentrum fur Luft- und Raumfahrt e.V. - DLR) and from his environment. We are providing libraries with a part of the circulation. Outstanding scientific contributions and dissertations are here published as well as projects reports and proceedings of conferences in our house with different contributors from science, economy and politics. With this series we are pursuing the objective to enable a broad access to scientific works and results. We are using the series as well as to promote practically young researchers by the publication of the dissertation of our staff and external doctoral candidates, too. Publications are important milestones on the academic career path. With the series „Reports of the DLR-Institute of Transportation Systems / Berichte aus dem DLR-Institut fur Verkehrssystem¬technik“ we are widening the spectrum of possible publications with a bulding block. Beyond that we understand the communication of our scientific fields of research as a contribution to the national and international research landscape in the fiels of automotive, railway systems and traffic management. This volume contains the proceedings of the SUMO2015 – Intermodal Simulation for Intermodal Transport Data, which was held from 7th to 8th May 2015 in Berlin-Adlershof, Germany. SUMO is a well established microscopic traffic simulation suite which has been available since 2002 and provides a wide range of traffic planning and simulation tools. The conference proceedings give a good overview of the applicability and usefulness of simulation tools like SUMO ranging from new methods in traffic control and vehicular communication to the simulation of complete cities. Another aspect of the tool suite, its universal extensibility due to the availability of the source code, is reflected in contributions covering rapid scenario prototyping and interfacing improvements to govern microscopic traffic simulation results. The major topic of this third edition of the SUMO conference is the interaction of different types of traffic and intermodality. Several articles cover heterogeneous traffic networks as well as logistics and pedestrian extensions to the simulation. Subsequent specialized issues such as disaster management aspects and applying agile development techniques to scenario building are targeted as well. The conference’s aim was bringing together the large international user community and exchanging experience in using SUMO, while presenting results or solutions obtained using the software or modeling mobility with open data. Let you inspire to try your next project with the SUMO suite. There are many new applications in your environment. Prof. Dr.-Ing. Karsten Lemmer

Deep Reinforcement Learning for Autonomous Driving: A Survey

Abstract: With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.

Deep Reinforcement Learning for Autonomous Driving: A Survey

Abstract: With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Abstract: Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.

TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors

Abstract: Simulation has the potential to massively scale evaluation of self-driving systems, enabling rapid development as well as safe deployment. Bridging the gap between simulation and the real world requires realistic multi-agent behaviors. Existing simulation environments rely on heuristic-based models that directly encode traffic rules, which cannot capture irregular maneuvers (e.g., nudging, U-turns) and complex interactions (e.g., yielding, merging). In contrast, we leverage real-world data to learn directly from human demonstration, and thus capture more naturalistic driving behaviors. To this end, we propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we parameterize the policy with an implicit la-tent variable model that generates socially-consistent plans for all actors in the scene jointly. To learn a robust policy amenable for long horizon simulation, we unroll the policy in training and optimize through the fully differentiable simulation across time. Our learning objective incorporates both human demonstrations as well as common sense. We show TrafficSim generates significantly more realistic traffic scenarios as compared to a diverse set of baselines. Notably, we can exploit trajectories generated by TrafficSim as effective data augmentation for training better motion planner.