Abstract
The increased mobility requirements of modern lifestyles put more stress on existing traffic infrastructure, which causes reduced traffic flow, especially in peak traffic hours. This calls for new and advanced solutions in traffic flow regulation and management. One approach towards optimisation is a transition from static to dynamic traffic light intervals, especially in spots where pedestrian crossing cause stops in road traffic flow. In this paper, we propose a smart pedestrian traffic light triggering mechanism that uses a Frequency-modulated continuous-wave (FMCW) radar for pedestrian detection. Compared to, for example, camera-surveillance systems, radars have advantages in the ability to reliably detect pedestrians in low-visibility conditions and in maintaining privacy. Objects within a radar’s detection range are represented in a point cloud structure, in which pedestrians form clusters where they lose all identifiable features. Pedestrian detection and tracking are completed with a group tracking (GTRACK) algorithm that we modified to run on an external processor and not integrated into the used FMCW radar itself. The proposed prototype has been tested in multiple scenarios, where we focused on removing the call button from a conventional pedestrian traffic light. The prototype responded correctly in practically all cases by triggering the change in traffic signalization only when pedestrians were standing in the pavement area directly in front of the zebra crossing.
Highlights
The demands and expectations of transportation infrastructure users and the complexity of traffic regulation and control in modern cities are driving the need to include novel, advanced solutions into traffic flow optimisation and management [1,2,3]
Pedestrian detection and tracking are completed with a group tracking (GTRACK) algorithm that we modified to run on an external processor and not integrated into the used Frequency-modulated continuous-wave (FMCW) radar itself
All urban traffic optimisation and management depends on the feedback signal from sensors, while video-surveillance systems, coupled with autonomous artificial intelligence (AI)-driven decision algorithms, are being actively pursued [4,5], various solutions based on different sensors [6] are commonly applied for different categories of traffic participants
Summary
The demands and expectations of transportation infrastructure users and the complexity of traffic regulation and control in modern cities are driving the need to include novel, advanced solutions into traffic flow optimisation and management [1,2,3]. All urban traffic optimisation and management depends on the feedback signal from sensors, while video-surveillance systems, coupled with autonomous artificial intelligence (AI)-driven decision algorithms, are being actively pursued [4,5], various solutions based on different sensors [6] are commonly applied for different categories of traffic participants. Which are the focus of this paper, technologies that are seeing increasingly widespread use are the already mentioned video-surveillance traffic systems. These allow for the simultaneous and accurate traffic monitoring of several different traffic areas used by different traffic participants, and can be quickly and accurately modified [6]. This type of system is very cost-effective, especially in highly specialised cases, such as distinguishing between different traffic participants
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