Joint Estimation of the Distance and Relative Velocity of Obstacles via Smartphone Active Sound Sensing for Pedestrian Safety

In this study, we proposed a new method for pedestrian safety named ObsSense that estimates the distance and relative velocity of both static and mobile roadside obstacles using probing signals emitted by an off-the-shelf smartphone carried by a pedestrian. Sound-based estimation of the movement properties of an obstacle has been actively studied in pervasive computing, and generally provides the distance from a smartphone user to an obstacle and the relative velocity of the obstacle. However, most prior studies have focused on estimating the properties of either static or mobile obstacles. Furthermore, they estimated either the distance to obstacles or their relative velocity. Nonetheless, both the distance and relative velocity of the oncoming obstacles are necessary to derive the timing of a possible collision. To address this issue, we proposed a new probing signal composed of sine waves and sine sweeps, designed to acquire information about both the distance and relative velocity of static and mobile obstacles. In addition, we proposed a neural network model that can jointly predict distance and relative velocity from reflected sounds of the probing signal captured by a smartphone. The proposed neural network was designed to consider the mobility status of obstacles, i.e., whether they are mobile or static, in order to select the most appropriate method to estimate their distance and relative velocity. In addition, our method uses the relationship between distance and velocity to increase the precision of its estimations. For example, the distance can be estimated by integrating the velocity-time curve. The performance of ObsSense was evaluated using real-world data, and the experimental results demonstrated the effectiveness of our proposed probing signal and neural network architecture.