Localization is one of the key components in the operation of self-driving cars. Owing to the noisy global positioning system (GPS) signal and multipath routing in urban environments, a novel, practical approach is needed. In this study, a sensor fusion approach for self-driving cars was developed. To localize the vehicle position, we propose a particle-aided unscented Kalman filter (PAUKF) algorithm. The unscented Kalman filter updates the vehicle state, which includes the vehicle motion model and non-Gaussian noise affection. The particle filter provides additional updated position measurement information based on an onboard sensor and a high definition (HD) map. The simulations showed that our method achieves better precision and comparable stability in localization performance compared to previous approaches.
In this study, a control algorithm is proposed to enhance the braking performance for an Autonomous Emergency Braking (AEB) system by improving the method for calculating the brake-application time when the vehicle is on an incline. The conventional AEB system in an algorithm-applied vehicle is limited because the gradient is not considered. With such systems, only a flat road environment is considered in terms of the road settings. To improve the braking performance on an incline, an AEB algorithm considering the road gradient is developed. A new calculation method for the brakeapplication time is proposed on the basis of the maximum deceleration that a vehicle can obtain on a road, and this is done by analyzing the force exerted on a vehicle that is on anincline. We confirmed that the AEB algorithm proposed in this paper improves the braking performance compared to the conventional AEB algorithm.
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