Castaño Fernando, Beruvides Gerardo, Haber Rodolfo E, Artuñedo Antonio
Centre for Automation and Robotics, Technical University of Madrid-Spanish National Research Council (UPM-CSIC), Ctra. Campo Real Km. 0.2, Arganda del Rey 28500, Spain.
Sensors (Basel). 2017 Sep 14;17(9):2109. doi: 10.3390/s17092109.
Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors' knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.
避撞是先进驾驶辅助系统中的一项重要功能，旨在在即将发生碰撞（与物体、车辆、行人等）之前提供正确、及时且可靠的警告。障碍物识别库的设计与实现旨在解决交通信息物理系统中障碍物检测的设计与评估问题。该库被集成到一个协同仿真框架中，该框架在SCANeR软件与Matlab/Simulink之间的交互上得到支持。据作者所知，本文报道了两项主要贡献。首先，针对交通场景，提出了在信息物理系统中对虚拟片上光检测和测距传感器进行建模与仿真的方法。该信息物理系统在SCANeR中进行设计与实现。其次，还利用SCANeR提供的传感信息数据库，设计并应用了三种基于人工智能的特定障碍物识别库方法。该计算库有三种障碍物检测方法：多层感知器神经网络、自组织映射和支持向量机。最后，给出了这些方法在不同天气条件下的比较结果，在准确性方面取得了非常有前景的成果。在晴天和雾天条件下使用多层感知器取得了最佳结果，在雨天条件下使用支持向量机，在雪天条件下使用自组织映射。
