Enhancing the realism of autonomous driving simulation with real-time co-simulation

Qiwei Chen; Tiexin Wang; Chengjie Lu; Tao Yue; Shaukat Ali

doi:10.1145/3550356.3561558

Enhancing the realism of autonomous driving simulation with real-time co-simulation

Qiwei Chen
, Tiexin Wang
, Chengjie Lu
, Tao Yue
, Shaukat Ali

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Autonomous driving simulators are commonly used to develop autonomous driving systems (ADS) since they provide the flexibility to experiment with scenarios that could even be dangerous in a real setting. This flexibility, however, comes with the possibility of experimenting with unrealistic scenarios. To this end, we present an initial co-simulation framework integrating OpenModelica and CARLA to enable real-time communication between them. As a proof of concept, we experimented with two Modelica models (air resistance and energy consumption). We connected the two models with CARLA to enable real-time communication between them to ensure the realism of scenarios in addition to connecting CARLA with OpenWeather through its API to access real weather conditions. We conducted experiments with a specific virtual electric vehicle (Tesla Model 3) running on the Town06 map in CARLA. Results provide preliminary evidence that co-simulation with Modelica models improved the realism of the virtual vehicle in CARLA.

Original language	English
Title of host publication	Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022
Subtitle of host publication	Companion Proceedings
Publisher	Association for Computing Machinery, Inc
Pages	659-667
Number of pages	9
ISBN (Electronic)	9781450394673
DOIs	https://doi.org/10.1145/3550356.3561558
State	Published - 23 Oct 2022
Externally published	Yes
Event	25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2022 - Montreal, Canada Duration: 23 Oct 2022 → 28 Oct 2022

Publication series

Name	Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings

Conference

Conference	25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2022
Country/Territory	Canada
City	Montreal
Period	23/10/22 → 28/10/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

autonomous vehicles
Multi-paradigm modeling
Real-time co-simulation

Access to Document

10.1145/3550356.3561558

Cite this

Chen, Q., Wang, T., Lu, C., Yue, T., & Ali, S. (2022). Enhancing the realism of autonomous driving simulation with real-time co-simulation. In Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings (pp. 659-667). (Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings). Association for Computing Machinery, Inc. https://doi.org/10.1145/3550356.3561558

Chen, Qiwei ; Wang, Tiexin ; Lu, Chengjie et al. / Enhancing the realism of autonomous driving simulation with real-time co-simulation. Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings. Association for Computing Machinery, Inc, 2022. pp. 659-667 (Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings).

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title = "Enhancing the realism of autonomous driving simulation with real-time co-simulation",

abstract = "Autonomous driving simulators are commonly used to develop autonomous driving systems (ADS) since they provide the flexibility to experiment with scenarios that could even be dangerous in a real setting. This flexibility, however, comes with the possibility of experimenting with unrealistic scenarios. To this end, we present an initial co-simulation framework integrating OpenModelica and CARLA to enable real-time communication between them. As a proof of concept, we experimented with two Modelica models (air resistance and energy consumption). We connected the two models with CARLA to enable real-time communication between them to ensure the realism of scenarios in addition to connecting CARLA with OpenWeather through its API to access real weather conditions. We conducted experiments with a specific virtual electric vehicle (Tesla Model 3) running on the Town06 map in CARLA. Results provide preliminary evidence that co-simulation with Modelica models improved the realism of the virtual vehicle in CARLA.",

keywords = "autonomous vehicles, Multi-paradigm modeling, Real-time co-simulation",

author = "Qiwei Chen and Tiexin Wang and Chengjie Lu and Tao Yue and Shaukat Ali",

note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2022 ; Conference date: 23-10-2022 Through 28-10-2022",

year = "2022",

month = oct,

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doi = "10.1145/3550356.3561558",

language = "英语",

series = "Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings",

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}

Chen, Q, Wang, T, Lu, C, Yue, T & Ali, S 2022, Enhancing the realism of autonomous driving simulation with real-time co-simulation. in Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings. Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings, Association for Computing Machinery, Inc, pp. 659-667, 25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2022, Montreal, Canada, 23/10/22. https://doi.org/10.1145/3550356.3561558

Enhancing the realism of autonomous driving simulation with real-time co-simulation. / Chen, Qiwei; Wang, Tiexin; Lu, Chengjie et al.
Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings. Association for Computing Machinery, Inc, 2022. p. 659-667 (Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Enhancing the realism of autonomous driving simulation with real-time co-simulation

AU - Chen, Qiwei

AU - Wang, Tiexin

AU - Lu, Chengjie

AU - Yue, Tao

AU - Ali, Shaukat

PY - 2022/10/23

Y1 - 2022/10/23

N2 - Autonomous driving simulators are commonly used to develop autonomous driving systems (ADS) since they provide the flexibility to experiment with scenarios that could even be dangerous in a real setting. This flexibility, however, comes with the possibility of experimenting with unrealistic scenarios. To this end, we present an initial co-simulation framework integrating OpenModelica and CARLA to enable real-time communication between them. As a proof of concept, we experimented with two Modelica models (air resistance and energy consumption). We connected the two models with CARLA to enable real-time communication between them to ensure the realism of scenarios in addition to connecting CARLA with OpenWeather through its API to access real weather conditions. We conducted experiments with a specific virtual electric vehicle (Tesla Model 3) running on the Town06 map in CARLA. Results provide preliminary evidence that co-simulation with Modelica models improved the realism of the virtual vehicle in CARLA.

AB - Autonomous driving simulators are commonly used to develop autonomous driving systems (ADS) since they provide the flexibility to experiment with scenarios that could even be dangerous in a real setting. This flexibility, however, comes with the possibility of experimenting with unrealistic scenarios. To this end, we present an initial co-simulation framework integrating OpenModelica and CARLA to enable real-time communication between them. As a proof of concept, we experimented with two Modelica models (air resistance and energy consumption). We connected the two models with CARLA to enable real-time communication between them to ensure the realism of scenarios in addition to connecting CARLA with OpenWeather through its API to access real weather conditions. We conducted experiments with a specific virtual electric vehicle (Tesla Model 3) running on the Town06 map in CARLA. Results provide preliminary evidence that co-simulation with Modelica models improved the realism of the virtual vehicle in CARLA.

KW - autonomous vehicles

KW - Multi-paradigm modeling

KW - Real-time co-simulation

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BT - Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022

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Y2 - 23 October 2022 through 28 October 2022

ER -

Chen Q, Wang T, Lu C, Yue T, Ali S. Enhancing the realism of autonomous driving simulation with real-time co-simulation. In Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings. Association for Computing Machinery, Inc. 2022. p. 659-667. (Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings). doi: 10.1145/3550356.3561558

Enhancing the realism of autonomous driving simulation with real-time co-simulation

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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