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Stability Control of Autonomous Vehicles with Four In-Wheel Motor Drive for Severe Environments
Technical Paper
2017-01-2001
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Research and development of autonomous functions for a road vehicle become increasingly active in recent years. However, the vehicle driving dynamics performance and safety are the big challenge for the development of autonomous vehicles especially in severe environments. The optimum driving dynamics can only be achieved when the traction torque on all wheels can be influenced and controlled precisely. In this study, we present a novel approach to this problem by designing an advanced torque vectoring controller for an autonomous vehicle with four direct-drive in-wheel motors to generate and control the traction torque and speed quickly and precisely, thus to improve the stability and safety of the autonomous vehicle. A four in-wheel motored autonomous vehicle equipped with Radar and camera is modelled in PanoSim software environment. Vehicle-to-Vehicle (V2V) communication is used in this software platform to avoid collision. Individual in-wheel motor control systems are integrated and networked together using a high-level advanced vectoring control system. The proposed vectoring control system can monitor and manage the behavior of the individual subsystems, assigning appropriate tasks to each of them according to the driving maneuver and road conditions. The performance and effectiveness of the proposed vectoring control system is evaluated using standard test maneuvers. Simulation results show that the proposed advanced torque vectoring controller can improve the vehicle steadiness and transient response properties, thereby enhancing the stability performance compared with the conventional central motor controller particularly for severe environment conditions.
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Citation
Li, X., Situ, L., Yu, Y., and Chen, F., "Stability Control of Autonomous Vehicles with Four In-Wheel Motor Drive for Severe Environments," SAE Technical Paper 2017-01-2001, 2017, https://doi.org/10.4271/2017-01-2001.Data Sets - Support Documents
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| Unnamed Dataset 1 |
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