Using Attention Mechanisms in Compact CNN Models for Improved Micromobility Safety Through Lane Recognition

Chinmaya Kaundanya, Paulo Cesar, Barry Cronin, Andrew Fleury, Mingming Liu, Suzanne Little

Abstract: The use of personal transportation devices such as e-bikes and e-scooters (micromobility) necessitates the development of improved safety support systems using highly-accurate, real-time lane recognition. However, the constrained operating environment, both computationally and physically, on such devices restricts the applicability of existing sensor-based solutions. One option is to leverage vision-based systems and AI models. However, these are typically built using high-spec processors and high-memory platforms and the models need to be adapted to low-spec platforms such as microcontrollers. A significant barrier to the development and evaluation of these potential solutions is the lack of lane recognition datasets that focus on the first-person (rider) perspective. We contribute a lane recognition dataset of micromobility first-person perspective images from e-mobility rides. This dataset is utilized to assess the impact of channel and spatial attention on compact CNN models, dri

External IDs:dblp:conf/vehits/KaundanyaCCFLL24