Robust object segmentation using a multi-layer laser scanner.

The major problem in an advanced driver assistance system (ADAS) is the proper use of sensor measurements and recognition of the surrounding environment. To this end, there are several types of sensors to consider, one of which is the laser scanner. In this paper, we propose a method to segment the measurement of the surrounding environment as obtained by a multi-layer laser scanner. In the segmentation, a full set of measurements is decomposed into several segments, each representing a single object. Sometimes a ghost is detected due to the ground or fog, and the ghost has to be eliminated to ensure the stability of the system. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments show that the proposed method demonstrates good performance in many real-life situations.

Data augmentation using generative models for track intrusion detection.

The objective of this work is to address the problem of detecting track intruders in railway systems using deep learning-based algorithms. Unauthorized entry onto railway tracks poses a significant risk of collisions between trains and humans. However, intrusion discrimination algorithms often suffer from a lack of learning data and data imbalance issues. To overcome these challenges, this research proposes an algorithm that combines generative models and classification networks. Generative models are utilized to generate synthetic intrusion data by learning the underlying distribution of available data and creating new samples resembling the original data. The augmented intrusion data is then used to train deep neural networks to accurately identify intrusions. The proposed algorithm is evaluated using real data sets, demonstrating its effectiveness in overcoming limited learning data and data imbalance issues. By augmenting intrusion data using generative models, the algorithm achieves improved accuracy compared to traditional approaches. In conclusion, the algorithm presented in this work provides a solution for detecting track intruders in railway systems. By leveraging generative models to augment limited intrusion data and utilizing classification networks for intrusion discrimination, the algorithm demonstrates improved performance in accurately identifying intrusions. This research highlights the potential of deep learning-based approaches in enhancing railway safety and recommends further exploration and application of these methods in real-world settings.