Distracted pedestrians, like distracted drivers, are an increasingly dangerous threat and precursors to pedestrian accidents in urban communities, often resulting in grave injuries and fatalities. Mitigating such hazards to pedestrian safety requires employment of pedestrian safety systems and applications that are effective in detecting them. Designing such frameworks is possible with the availability of sophisticated mobile and wearable devices equipped with high-precision on-board sensors capable of capturing fine-grained user movements and context, especially distracted activities. However, the key technical challenge is accurate recognition of distractions with minimal resources in real-time given the computation and communication limitations of these devices. Several recently published works improve distracted pedestrian safety by leveraging on complex activity recognition frameworks using mobile and wearable sensors to detect pedestrian distractions. Their primary focus, however, was to achieve high detection accuracy, and therefore most designs are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system, we design an efficient and real-time pedestrian distraction detection technique that overcomes some of these shortcomings. We demonstrate its practicality by implementing prototypes on commercially-available mobile and wearable devices and evaluating them using data collected from participants in realistic pedestrian experiments. Using these evaluations, we show that our technique achieves a favorable balance between computational efficiency, detection accuracy and energy consumption compared to some other techniques in the literature.
Techniques for aiding pedestrian safety that do not rely on camera input, but rather on a smartphone's microphone @cite_12 and GPS @cite_6 have also been proposed. For instance, @cite_12 employ sound features extracted from the smartphone's microphone to detect oncoming vehicles, while @cite_6 recognizes potential collisions between pedestrians and oncoming vehicles using the smartphone's GPS. One main shortcoming of these systems is that they are useful in detecting only outdoor traffic-related hazards scenarios.
@cite_6 @cite_12
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We implement a collision prevention system, called pSafety, which instantaneously informs pedestrians and drivers of the potential threatening accidents. Unlike other systems, pSafety alerts pedestrians of threatening vehicles coming from not only the line-of-sight, but also non-line-of-sight due to obstructions of the wall corner or other vehicles. pSafety collects GPS information from smartphones of pedestrians and vehicle drivers through mobile networks. The main challenge of pSafety is that current smartphones demonstrate larger distance errors on the order of a few meters due to its intrinsic low-cost GPS receivers. To address the impact of large-error positioning to pSafety, we regard each participant on the map as a sector that indicates a predicted location. We subsequently design the Sector Overlap Detection Algorithm, called SODA, to detect whether two sectors are overlapping in time O(1). To avoid warning fatigue, we additionally provide a threat ranking method to evaluate the degree of risk for each potential collision event. Through our designed App, pedestrians and drivers both could receive a clear view of potential risks and then take proper actions to avoid accidents. In our implementation, we show that pSafety rapidly informs participants (i.e., pedestrians and drivers) and provides each participant a sufficient response time to avoid collision. Pedestrians' use of Motion Pictures Expert Group audio layer 3 players or mobile phones can pose the risk of being hit by motor vehicles. We present an approach for detecting a crash risk level using the computing power and the microphone of mobile devices that can be used to alert the user in advance of an approaching vehicle so as to avoid a crash. A single feature extractor classifier is not usually able to deal with the diversity of risky acoustic scenarios. In this paper, we address the problem of detection of vehicles approaching a pedestrian by a novel simple nonresource intensive acoustic method. The method uses a set of existing statistical tools to mine signal features. Audio features are adaptively thresholded for relevance and classified with a three-component heuristic. The resulting acoustic hazard detection system has a very low false-positive detection rate. The results of this study could help mobile device manufacturers to embed the presented features into future potable devices and contribute to road safety.