Virtual intelligent vehicle urban simulator: Application to vehicle platoon evaluation
Introduction
Testing algorithms with real cars is a mandatory step in developing new intelligent abilities for future transportation systems. However, this step is sometimes hard to accomplish especially due to hardware and vehicle availability problems. Moreover, it is also difficult to reproduce the same scenario several times since some experimental parameters are variable and uncontrollable such as perception conditions. Thus, it is hard to compare several algorithms using the same experimental conditions. Besides, some critical and/or forbidden scenarios (i.e. scenario that implies the collision and/or destruction of a part of the physical device) are difficult to test in the real world.
Thus, it seems important to find a less restrictive way to perform scenarios with near reality conditions. That’s why many laboratories are testing their algorithms in simulation. The main problem of standard simulation tools is their distance from real conditions, since they do not generally take into account the vehicle sensors, physical model, infrastructure characteristics and their interactions. Virtual cameras are basically reduced to a simple pinhole model without distortion simulation and vehicle models do not take into account any dynamical characteristics and tyre-road contact with the exception of in specific automotive industry tools.
The participation of the SeT Laboratory to cristal project1 has demonstrated that a new simulation/prototyping tool is required to perform a comparison between several solutions in the context of urban intelligent vehicles. For instance, a comparison between linear platoon solutions has been made. In order to obtain realistic results, this tool must be as precise as possible in terms of sensors simulation and vehicle dynamics.
This paper presents the global architecture of the simulation/prototyping tool named Virtual Intelligent Vehicle Urban Simulator2 (vivus) developed by the SeT Laboratory. This simulator is aimed at simulating vehicles and sensors, taking into account their physical properties and prototyping artificial intelligence algorithms such as platoon solutions [1] and obstacle avoidance devices [2]. The goal of vivus is thus to overcome the general drawbacks of classical solutions by providing the possibility of designing a vehicle virtual prototype with simulated embedded sensors. The retained solution has several interests such as:
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Prototyping artificial intelligence algorithms before the construction of the first vehicle prototype. In this case, they can be developed, tested and tuned with a virtual prototype of a real vehicle.
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Testing critical and/or banned use cases, i.e., cases that imply partial or total destruction of vehicles, to overcome the limits of the retained solutions.
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Testing and comparing several algorithms/solutions for embedded features with a low development cost. It can thus help in choosing the future embedded devices related to retained solutions requirements (processing power needs, connectivity, etc.).
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Testing and comparing the sensor solutions before integrating them into the vehicle.
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Integrating tests and evaluations results into the vehicle design process.
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Using informed and documented virtual reality to access the attribute and state values of the vehicle, its components, and the environmental objects around it.
This tool has been especially used during the cristal project to compare several linear platoon algorithms with the results obtained with a real vehicle. Parts of these experiments are exposed in this paper in order to illustrate the similarities between the simulation’s and real car’s results.
The paper is structured as follows. The next section presents a state of the art of the simulation applications with a comparison according to several criteria. Section 3 gives a description of vivus architecture focusing on physics model and virtual sensors description. Then, Section 4 gives a short presentation of the targeted application, namely linear platoon evaluation, on which the developed simulator has been tested. Section 5 exposes results obtained when comparing simulator and real vehicle experiments. Finally, Section 6 concludes the paper.
Section snippets
Simulation tools comparison
Computer simulations and their extension into robot and vehicle simulations are an important topic. The main motivation for using simulation tools is the potential to validate new technology before its deployment in real devices. The validation aims at assessing the compliance of a system according to the design objectives. Generally, this evaluation is done by submitting the system (or a model that represents it more or less accurately) to a series of tests. The quality of test cases
Simulator model
This part presents the simulator model. After a global overview of both the simulator architecture and running process, this section will focus on specific components such as the physics model and the sensor models.
Comparison methodology
As expressed in the introduction the main goal of our project is to develop a reliable simulation tool able to test new intelligent vehicle algorithm and to perform impossible scenario (crash, extreme tyre/road contact conditions, …). In order to validate the choices made in this simulator, we compare the results obtained in simulation with those obtained with real cars. To do so, we take as an example a well-known application in our laboratory and for which results have already been published
Simulation and experimental protocol
Based on the algorithm described in the previous section, simulation and experimentation scenarios are designed and performed to check platoon evolution during lateral displacement situations.
Simulations are realized with the vivus simulator (Fig. 4 (left)) presented in this paper. The second platform is composed of GEM electrical vehicles modified by the ”Systèmes et Transports” Laboratory (Fig. 4 (right)). These vehicles have been automated and can be controlled by an onboard system.
Conclusion and perspectives
This paper presents an urban vehicle simulator based on two main engines: one for physics simulation, and one for 3D immersion in a topological environment. The use of these engines allows a precise simulation of vehicle dynamics and a wide range of physical sensors. This sensor’s simulation is split into two parts: the low-level sensors that extract information from physics and 3D models; and high-level sensors, which are formatting data coming from the low-level sensors to fit the physical
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