In a typical railyard environment, a myriad of large and dynamic objects pose significant risks to railyard workers. Unintentional falls, trips and collisions with dynamic rolling stock due to distractions or lack of situational awareness are an unfortunate reality in modern railyards. The challenges of current technologies in detecting and tracking multiple differently-sized mobile objects in situations such as i) one-on-one, ii) many-to-one, iii) one-to-many, iv) blind spot, and v) interfering/non-interfering separation creates the possibility for reduction or loss of situational awareness in this fast-paced environment. The simultaneous tracking of assets with different size, velocity and material composition in different working and environmental conditions can only be accomplished through joint infrastructure-based asset discovery and localization sensors that cause no interference or impediment to the railyard workers, and which are capable of detecting near-misses as well. Our team is investigating the design and performance of such a solution, and is currently focusing on the innovative usage of lightweight low-cost RADAR under different conditions that are expected to be encountered in railyards across North America. We are employing Ancorteks 580-AD Software Defined RADAR (SDRadar) system, which operates at the license-free frequency of 5.8 GHz and with a variety of different configuration options that make it well-suited for generalized object tracking. The challenges, however, stem from the unique interplay between tracking large metallic objects such as railcars, locomotives, and trucks, as well as smaller objects such as railyard workers, in particular their robust discernment from each other. Our design’s higher-level system can interact with the lower-level SDRadar design to change the parameters in real-time to detect and track large objects over significant distances. The algorithm optimally adjusts waveform, sweep time and sample rate based on one or multiple detected object cross-sections and subsequently alters these parameters to be able to discern other objects from them that are in close proximity. We also use an ensemble method to determine the velocity and distance of target objects to accurately track the subject and larger objects at a distance. The methodology has been field-tested with several test cases in a multitude of weather and lighting conditions. We have also tested the proper height, azimuth and elevation angles for positioning our SDRadar to alleviate the risk of blind spots and enhancing the detection and tracking capabilities of our algorithm. The approach has outperformed our previous tests using visual and acoustic sensors for detection and tracking railroad workers in terms of accuracy and operating flexibility. In this paper, we discuss the details of our proposed approach and present our results from the field tests.
A THREE-LEVEL ARCHITECTURE FOR MODEL–FREE DETECTION AND TRACKING OF INDEPENDENTLY MOVING OBJECTS