Synchrono: A Scalable, Physics-Based Simulation Platform for Testing Groups of Autonomous Vehicles And/or Robots

2021 ◽  
Author(s):  
Jay Taves ◽  
Asher Elmquist ◽  
Aaron Young ◽  
Dan Negrut ◽  
Radu Serban
Keyword(s):  
Autonomous Vehicles ◽  
Simulation Platform ◽  
Physics Based Simulation

Enabling Artificial Intelligence Studies in Off-Road Mobility Through Physics-based Simulation of Multi-Agent Scenarios

2021 ◽  
Author(s):  
Aaron Young ◽  
Jay Taves ◽  
Asher Elmquist ◽  
Simone Benatti ◽  
Alessandro Tasora ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Message Passing ◽  
Message Passing Interface ◽  
Autonomous Agents ◽  
Time Space ◽  
Intelligence Studies ◽  
Multi Agent ◽  
Physics Based Simulation ◽  
Design And Testing

Abstract We describe a simulation environment that enables the design and testing of control policies for off-road mobility of autonomous agents. The environment is demonstrated in conjunction with the training and assessment of a reinforcement learning policy that uses sensor fusion and inter-agent communication to enable the movement of mixed convoys of human-driven and autonomous vehicles. Policies learned on rigid terrain are shown to transfer to hard (silt-like) and soft (snow-like) deformable terrains. The environment described performs the following: multi-vehicle multibody dynamics co-simulation in a time/space-coherent infrastructure that relies on the Message Passing Interface standard for low-latency parallel computing; sensor simulation (e.g., camera, GPU, IMU); simulation of a virtual world that can be altered by the agents present in the simulation; training that uses reinforcement learning to 'teach' the autonomous vehicles to drive in an obstacle-riddled course. The software stack described is open source.

Enabling Artificial Intelligence Studies in Off-Road Mobility Through Physics-Based Simulation of Multi-Agent Scenarios

2021 ◽  
Author(s):  
Aaron Young ◽  
Jay Taves ◽  
Asher Elmquist ◽  
Radu Serban ◽  
Dan Negrut ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Agents ◽  
Simulation Environment ◽  
Agent Communication ◽  
Testing Environment ◽  
Simulation Engine ◽  
Intelligence Studies ◽  
Multi Agent ◽  
Physics Based Simulation ◽  
Sensor Modeling

Abstract We describe a simulation environment that enables the development and testing of control policies for off-road mobility of autonomous agents. The environment is demonstrated in conjunction with the design and assessment of a reinforcement learning policy that uses sensor fusion and inter-agent communication to enable the movement of mixed convoys of human-driven and autonomous vehicles. Policies are learned on rigid terrain and are subsequently shown to transfer successfully to hard (silt-like) and soft (snow-like) deformable terrains. The enabling simulation environment is developed from the high fidelity, physics-based simulation engine Chrono. Five Chrono modules are employed herein: Chrono::Engine, Chrono::Vehicle, PyChrono, SynChrono and Chrono::Sensor. Vehicle’s are modeled using Chrono::Engine and Chrono::Vehicle and deployed on deformable terrain within the training/testing environment. Utilizing the Python interface to the C++ Chrono API called PyChrono and OpenAI Gym’s supporting infrastructure, training is conducted in a GymChrono learning environment. The GymChrono-generated policy is subsequently deployed for testing in SynChrono, a scalable, cluster-deployable multi-agent testing infrastructure built on MPI. SynChrono facilitates inter-agent communication and maintains time and space coherence between agents. A sensor modeling tool, Chrono::Sensor, supplies sensing data that is used to inform agents during the learning and inference processes. The software stack and the Chrono simulator are both open source. Relevant movies: [1].

A Flexible Multi-Modal Multi-User Traffic Simulation for Studying Complex Road Design

2020 ◽  
Author(s):  
Jack Miller ◽  
Vijay Kalivarapu ◽  
Michael Holm ◽  
Tor Finseth ◽  
Jordan Williams ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Traffic Accidents ◽  
Traffic Simulation ◽  
User Behavior ◽  
Low Cost ◽  
Simulation Platform ◽  
Commodity Hardware ◽  
Monitor Display ◽  
Head Mounted Display ◽  
Road Design

Abstract While there are many factors that affect traffic accidents, safe road design plays a key role. It is critical to understand traffic flow and driver response to various scenarios. Furthermore, with the inclusion of new traffic trends such as bicycle friendly roads and autonomous vehicles, it is ever more important to study the complex relationships between traffic entities. However, controlled experiments can be difficult to implement in the real world. Traffic simulation has been researched for decades to understand the flow of traffic in a variety of environments and how users interact within it. However, these platforms are limited in simulation and authoring capabilities, do not allow for multiple human agents, or are not accessible by roadway designers. This research explores the technical development of a multi-user multi-modal traffic simulation platform that expands on the capabilities of traditional traffic simulators. Traffic simulation of virtual cars and pedestrian agents were generated through VISSIM and networked into a simulation platform. Multiple human agents can interface with the system through a physical car and bicycle rig featuring both a virtual reality head mounted display and a multi-monitor display. Furthermore, the multi-user nature of the platform allows for a variety of complex research applications including multi-user behavior. By utilizing low-cost commodity hardware and software, the accessibility of this platform is greatly increased. A testbed environment was created to evaluate the technical limitations of the platform and assess where further work is needed. Through this evaluation, the authoring process and networking capabilities were optimized. Additionally, the core functionality of the platform, including the various simulator modes and multi-user functionality, was found to be successful. Through this research, combining commodity hardware with traffic simulation will allow a larger number of researchers to better understand traffic environments and how humans may interact within them.

Evaluating the Emission and Energy Impacts of Automated Buses on Urban Expressways

2020 ◽  
Vol 2674 (12) ◽  
pp. 515-529
Author(s):  
Yixin Zhang ◽  
Xumei Chen ◽  
Lei Yu
Keyword(s):  
Energy Consumption ◽  
Autonomous Vehicles ◽  
High Energy ◽  
Environmental Benefits ◽  
Specific Power ◽  
Simulation Platform ◽  
Dynamic Link Library ◽  
Operational Strategies ◽  
Save Energy ◽  
The Impact

In recent years, a series of traffic problems have emerged with continuously increasing traffic. Connected and autonomous vehicles (CAV) technology is considered to be an effective way to relieve these problems. It is believed that buses, trucks, and other special vehicles could be among of the first application areas to promote the development of CAV technology. Because of their features of high emissions of pollutants and high energy consumption, the improvement of environmental benefits for such heavy vehicles as buses is the focus in this research. Therefore, this paper aims to evaluate the impact of automated buses on emissions and energy consumption on urban expressways. To achieve the research objectives in this paper, the established automated buses model is embedded into the simulation platform with VISSIM dynamic link library. Models are developed for emissions and energy consumption calculations based on vehicle-specific power to quantify the environmental impact of automated buses. Two improvement strategies: dedicated managed lane and dedicated bus lane, are designed. Finally, a VISSIM simulation platform based on the Fourth Ring Road in Beijing (Xueyuan Bridge to Haidian Bridge) is built to conduct case studies. The results show that CAV technology in buses can reduce exhaust emissions and save energy. Moreover, the managed lane strategy brings a significant reduction in the emissions and energy consumption of automated buses. These findings can be used for the development of automated bus operational strategies focused on environmental benefits.

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