Cross-Platform Simulation Architecture with application to truck platooning impact assessment
Simulation-based traffic impact assessment studies of advanced technologies such as truck platooning need to be carried out to ascertain their benefits for traffic efficiency, safety and environment. To reduce uncertainty in the results of such simulation-based studies, the same simulation studies can be performed in different simulation software. Many traffic simulation software packages (Aimsun, SymuVia, Vissim, SUMO) are currently available for traffic impact assessment of new technologies such as truckplatooning. However, to fully model and simulate the functionalities of such advanced technologies in different simulation environments, several extensions need to be made to the simulation platforms. In most cases, these extensions have to be programmed in different programming languages (C++, Python) and each simulator has its own simulator specific API. This makes it difficult to reuse software written for a specific functionality in one simulation platform in a different simulation platform. To overcome this issue, this paper presents a novel architecture for cross-platform simulation. The architecture is designed such that a specific functionality such as truck-platooning or any other functionality is made platform independent. We designed a cross-platform architecture for simulating a truck-platooning functionality using Vissim and SymuVia simulation software to determine the traffic flow effects of multi-brand truck platooning in the context of the EU project ENSEMBLE. In this draft paper, we present the structure of the framework as well as some preliminary results from a simple simulation performed with the cross-platform simulator.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A CARLA-based Simulation of Electrically Driven Forklifts
This paper presents the simulation of the operation of an electric forklift fleet within an intralogistics scenario. For this purpose, the open source simulation tool CARLA is used; according to our knowledge this is a n…
Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic
Balancing safety, efficiency, and operational costs in highway driving poses a challenging decision-making problem for heavy-duty vehicles. A central difficulty is that conventional scalar reward formulations, obtained b…
Reinforcement LearningDecision MakingSafe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidance
This paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable, and this approach enables the reference governor to gradual…
Longitudinal Deep Truck: Deep learning and deep reinforcement learning for modeling and control of longitudinal dynamics of heavy duty trucks
Heavy duty truck mechanical configuration is often tailor designed and built for specific truck mission requirements. This renders the precise derivation of analytical dynamical models and controls for these trucks from …
Deep Reinforcement LearningReinforcement Learning (RL)Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones
The role of unmanned vehicles for searching and localizing the victims in disaster impacted areas such as earthquake-struck zones is getting more important. Self-navigation on an earthquake zone has a unique challenge of…
NavigateSemantic Segmentation