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• W202342948 abstract "The nature of automobile dynamics is complex. While they might not be aware of it, thedriver of a vehicle is making many complex decisions producing a complex series ofactions that effect the motion of the vehicle. Usually the driver can perform a sequenceof actions which move the vehicle in a way in which the driver intends, however,occasionally all drivers find themselves having to correct the vehicle in a way that theydid not expect.The problem here is in the control system and the high performance available in thevehicles that are driven on the roads. With the brake pedal linked directly to the forceon the brake disks, the driver of the vehicle simply applies pressure that corresponds tothe rate at which they intend to stop. At the limits of tyre adhesion this breaks down asmore brake pressure fails the slow the vehicle quicker, and the vehicle actually takeslonger to stop.To produce safer vehicles, car developers and manufacturers have developed anti-lockbrakes and stability control systems. These state of the art systems monitor drivercommands that inherently reflect their intention and the behaviour of the vehicle. Whenthe vehicle behaves in a way that does not follow the driver's intent the systemintervenes and selectively applies braking, limits engine power or changes otherrelevant parameters to assist the driver in retaining control of the vehicle. Systems suchas these use mathematical models based on simplified assumptions of vehiclebehaviour. Because of this they are built to be robust, commercially available systemsfail to capitalise on the full performance potential of the vehicle. Since systems such asthese become active in emergency situations, every small gain in performance can makeup the difference between life and death.Neural networks, as emerging decision making tools offer another approach to theproblem of modelling non-linear dynamic, multi variable vehicle physics. Neuralnetworks use artificial intelligence to find relationships between inputs and outputs.These relationships are not assumed or based upon a simplified physical analysis, butare built based on the past experiences of the network.For automobile dynamic prediction a special vehicle 'Intelligent Car' was conceived,constructed and tested in real world driving conditions. Structured driving tests werecarried out gathering sufficient data to train ant test the potential of neural networks forthe application. The results from these tests represent some of the first outcomes frompreliminary research in the 'Intelligent Car' Project. This study outlines the design and development of the University of Tasmania's Intelligent Car together with results from training various neural network models topredict its brake forces and comparison with measured values." @default.
• W202342948 created "2016-06-24" @default.
• W202342948 creator A5015917796 @default.
• W202342948 date "2002-01-01" @default.
• W202342948 modified "2023-09-26" @default.
• W202342948 title "Estimation of brake force on an open wheel racing car using artificial neural networks" @default.
• W202342948 hasPublicationYear "2002" @default.
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