Co-simulation of the virtual vehicle in virtual traffic considering tactical driver decisions

Kaths, Jakob; Schott, Benedikt; Chucholowski, Frederic

doi:10.29007/qzg2

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Through our definitions, the ego-object can be implemented taking into account not only its characteristics (i.e. whether it is aggressive, impulsive, cooperative or strategic, autonomous, automated or human-operated [36], [37], [38], [39], [40]), but also what it is able to see and perceive, its cognitive aspects, and what it expects the other objects present in the scenario to behave (which does not always correspond to the truth) so that the ego-object can react accordingly. In this paper, we have taken advantage of the flexibility of the set theory to design a framework that may be adapted to any software and in any future research development.…”

Section: Discussionmentioning

confidence: 99%

Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations

Andreotti

Boyraz

Selpi

2021

IEEE Trans. Intell. Veh.

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This paper introduces a unified mathematical definition for describing commonly used terms encountered in systematical analysis of automated driving systems in mixedtraffic simulations. The most significant contribution of this work is in translating the terms that are clarified previously in literature into a mathematical set and function based format. Our work can be seen as an incremental step towards further formalisation of Domain-Specific-Language (DSL) for scenario representation. We also extended the previous work in the literature to allow more complex scenarios by expanding the model-incompliant information using set-theory to represent the perception capacity of the road-user agents. With this dynamic perception definition, we also support interactive scenarios and are not limited to reactive and pre-defined agent behavior. Our main focus is to give a framework to represent realistic roaduser behavior to be used in simulation or computational tool to examine interaction patterns in mixed-traffic conditions. We believe that, by formalising the verbose definitions and extending the previous work in DSL, we can support automatic scenario generation and dynamic/evolving agent behavior models for simulating mixed traffic situations and scenarios. In addition, we can obtain scenarios that are realistic but also can represent rare-conditions that are difficult to extract from field-tests and real driving data repositories.

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Section: Discussionmentioning

confidence: 99%

Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations

Andreotti

Boyraz

Selpi

2021

IEEE Trans. Intell. Veh.

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“…In general, the integration of microscopic traffic simulators with driving simulators is not a novel concept [18], [19]. However, previous studies have presented certain challenges, which our paper addresses and resolves [20]- [23]. Specifically, our approach ensures synchronized co-simulation in real-time, utilizing the same road network in both simulators and achieving an impressively low delay of 5 ms.…”

Section: Introductionmentioning

confidence: 99%

Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation

Ferrarotti,

Luca,

Santin

et al. 2024

IEEE Access

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Optimizing traffic dynamics in an evolving transportation landscape is crucial, particularly in scenarios where autonomous vehicles (AVs) with varying levels of autonomy coexist with human-driven cars. While optimizing Reinforcement Learning (RL) policies for such scenarios is becoming more and more common, little has been said about realistic evaluations of such trained policies. This paper presents an evaluation of the effects of AVs penetration among human drivers in a roundabout scenario, considering both quantitative and qualitative aspects. In particular, we learn a policy to minimize traffic jams (i.e., minimize the time to cross the scenario) and to minimize pollution in a roundabout in Milan, Italy. Through empirical analysis, we demonstrate that the presence of AVs can reduce time and pollution levels. Furthermore, we qualitatively evaluate the learned policy using a cutting-edge cockpit to assess its performance in nearreal-world conditions. To gauge the practicality and acceptability of the policy, we conduct evaluations with human participants using the simulator, focusing on a range of metrics like traffic smoothness and safety perception. In general, our findings show that human-driven vehicles benefit from optimizing AVs dynamics. Also, participants in the study highlight that the scenario with 80% AVs is perceived as safer than the scenario with 20%. The same result is obtained for traffic smoothness perception.

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“…Delay depends on the rate of data transfer between the two simulators. Usually, driver simulators run with very short simulation steps (from a maximum of 33 ms [11] up to 1 ms [15]). Smaller time steps have to be preferred to enhance the experience felt by the driver.…”

Section: Introductionmentioning

confidence: 99%

SUMO Roundabout Simulation with Human in the Loop

Previati

Mastinu

2023

SUMO Conf Proc

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Traffic simulators rely on calibrated driver models in order to reproduce human behavior in different traffic scenarios. Even if quite accurate results can be obtained, the actual interaction between human being and traffic cannot be completely reproduced. In particular, as automated vehicles are being developed, the human in the loop is required to understand whether drivers feel comfortable and safe in mixed traffic conditions. In recent years, dynamic driving simulators have been developed to test vehicles in complex or dangerous situations in safe and controlled environments. However, driving simulators are mostly devoted to the study of vehicle dynamics more than traffic situations.This paper presents an integration of SUMO with a high end dynamic driving simulator with the aim to study human reactions while negotiating a roundabout in mixed traffic conditions. SUMO is in charge of traffic simulation, while a full vehicle model is employed for the simulation of the dynamic of the human driven car. To allow a human to effectively drive the car, both simulation environments have to run in real time while exchanging the required information. Also, scenario graphics, sound and driving simulator feedback motion have to be accurately realized and synchronized with the simulations. A real-time server is employed for the synchronization of the different environments. As SUMO does not consider vehicle dynamics, particular attention is devoted to the a realistic reconstruction of trajectories and vehicle dynamics to be represented in the scenario. Some preliminary tests are shown where a panel of testers has been asked to negotiate the roundabout with different percentages of automated vehicles. The results of the tests show that drivers were able to perceive differences in the behavior of other vehicles and that the proposed approach is effective for understanding the feeling of comfort and safety of the human driver.

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Co-simulation of the virtual vehicle in virtual traffic considering tactical driver decisions

Cited by 5 publications

References 2 publications

Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations

Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations

Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation

SUMO Roundabout Simulation with Human in the Loop

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