Cyclists are considered to be vulnerable road users (VRUs) and need protection from potential collisions with cars and other vehicles induced by unsafe driving, dangerous road conditions, or weak cycling infrastructure. Integrating mmWave radars into cycling safety measures presents an efficient solution to this problem given their compact size, low power consumption, and low cost compared to other sensors. This paper introduces an mmWave radar-based bike safety system designed to offer real-time alerts to cyclists. The system consists of a low-power radar sensor affixed to the bicycle, connected to a micro-controller, and delivering a preliminary classification of detected obstacles. An efficient two-level clustering based on the accumulation of radar point clouds from multiple frames with a temporal projection from previous frames into the current frame is proposed. The clustering is followed by a coarse classification algorithm in which we use relevant features extracted from the resulting clusters. An annotated RadBike dataset composed of radar point cloud data synchronized with RGB camera images is developed to evaluate our system. The two-level clustering outperforms the DBSCAN algorithm, achieving a v-measure score of 0.91, compared to 0.88 with classical DBSCAN. Different classifiers, including decision trees, random forests, support vector machines (SVMs), and AdaBoost, have been assessed, with an overall accuracy of 87% for the three main object classes: four-wheeled, two-wheeled, and others. The system has the ability to improve rider safety on the road and substantially reduce the frequency of incidents involving cyclists.

Three-phase AC motor drives play a key role in several applications, including energy conversion and automotive. Mainly, three-phase AC motor drives operate as closed loop control systems, where accurate feedback measurement sent by the current sensors is crucial to guarantee the good operation of the system. However, current sensors are potentially subject to several malfunctions that significantly affect the performance of the drive system. Accordingly, this paper proposes an efficient method for current sensor fault detection, and identification in three-phase AC motor drive system using a 2D Convolutional Neural Network (CNN). The proposed approach does need any additional extra-hardware components, since it uses only the signals already sent by the motor drive closed loop control. Indeed, it utilizes the 2D trajectory graph of the normalized motor current vector as input to a novel CNN Autoencoder model, which is introduced for feature extraction and classification. The efficiency and generalization capabilities of the proposed CNN autoencoder (PCAE) are benchmarked against a standard CNN model and conventional CNN autoencoders. The lightweight architecture of the PCAE enables its real-time implementation on a Raspberry pi 4 with a 750w experimental setup induction motor. The experimental results highlight that the proposed PCAE model can effectively detect and classify ten types of current sensor faults, in addition to distinguishing the healthy operation case. Moreover, the proposed approach achieves superior accuracy (99%), compared with conventional CNN (95%) and standard CNN-Autoencoder (96%) models.