|dc.description.abstract||Automotive collision avoidance systems help the driver to avoid or mitigate a collision. The main objective of this project is to ﬁnd a methodology to improve the performance of Volvo’s automotive collision avoidance system by optimizing its conﬁgurable parameters. It is important that the parameter setting is chosen in such a way that the automotive collision avoidance system is not too sensitive to uncertainties. However, ﬁnding an optimal parameter setting is an overwhelmingly complex problem. Therefore, our approach is to make the problem tractable, by choosing speciﬁc and realistic uncertainties, deﬁning performance, and choosing a fundamental algorithm that describes and mimics Volvo’s automotive collision avoidance system. This approach preserves the foundation of the problem. The idea behind the methodology that solves this tractable problem is to ﬁnd, and exclude, all the parameter values that can cause undesired assistance intervention and, out of the remaining parameter values, ﬁnd the ones that prevent collision in the best way. This is done under the condition that the chosen realistic uncertainties can occur. To evaluate a parameter setting, data simulation is used. Due to the complexity of the simulation, eﬃcient optimization tools are not available. Therefore, we have created a surrogate model that mimics the behaviour of the simulation as closely as possible by using a response surface, in this case accomplished by a radial basis function interpolation. Through this surrogate model we have found a satisfying parameter setting to the tractable problem. The methodology has laid a promising foundation of ﬁnding the optimal parameter setting to Volvo’s automotive collision avoidance system.
Keywords: Simulation-based optimization, response surface methodology, radial basis functions, multi-objective optimization, Pareto optimal solutions, trigger edge, tunable parameters, false intervention, robustness, positive and negative performance scenarios||sv