This paper presents the initial developments of new hardware devices targeted for CAN (Controller Area Network) bus communications in forest machines. CAN bus is a widely used protocol for communications in the automobile area. It is also applied in industrial vehicles and machines due to its robustness, simplicity, and operating flexibility. It is ideal for forestry machinery producers who need to couple their equipment to a machine that allows the transportation industry to recognize the importance of standardizing communications between tools and machines. One of the problems that producers sometimes face is a lack of flexibility in commercialized hardware modules; for example, in interfaces for sensors and actuators that guarantee scalability depending on the new functionalities required. The hardware device presented in this work is designed to overcome these limitations and provide the flexibility to standardize communications while allowing scalability in the development of new products and features. The work is being developed within the scope of the research project “SMARTCUT—Remote Diagnosis, Maintenance and Simulators for Operation Training and Maintenance of Forest Machines”, to incorporate innovative technologies in forest machines produced by the CUTPLANT S.A. It consists of an experimental system based on the PIC18F26K83 microcontroller to form a CAN node to transmit and receive digital and analog messages via CAN bus, tested and validated by the communication between different nodes. The main contribution of the paper focuses on the presentation of the development of new CAN bus electronic control units designed to enable remote communication between sensors and actuators, and the main controller of forest machines.

This paper describes a real case implementation of an automatic pedestrian-detection solution, implemented in the city of Aveiro, Portugal, using affordable LiDAR technology and open, publicly available, pedestrian-detection frameworks based on machine-learning algorithms. The presented solution makes it possible to anonymously identify pedestrians, and extract associated information such as position, walking velocity and direction in certain areas of interest such as pedestrian crossings or other points of interest in a smart-city context. All data computation (3D point-cloud processing) is performed at edge nodes, consisting of NVIDIA Jetson Nano and Xavier platforms, which ingest 3D point clouds from Velodyne VLP-16 LiDARs. High-performance real-time computation is possible at these edge nodes through CUDA-enabled GPU-accelerated computations. The MQTT protocol is used to interconnect publishers (edge nodes) with consumers (the smart-city platform). The results show that using currently affordable LiDAR sensors in a smart-city context, despite the advertising characteristics referring to having a range of up to 100 m, presents great challenges for the automatic detection of objects at these distances. The authors were able to efficiently detect pedestrians up to 15 m away, depending on the sensor height and tilt. Based on the implementation challenges, the authors present usage recommendations to get the most out of the used technologies.