SafeRoute: Learning to Navigate Streets Safely in an Urban Environment

by   Sharon Levy, et al.

Recent studies show that 85 avoid harassment and assault. Despite this, current mapping tools do not empower users with information to take charge of their personal safety. We propose SafeRoute, a novel solution to the problem of navigating cities and avoiding street harassment and crime. Unlike other street navigation applications, SafeRoute introduces a new type of path generation via deep reinforcement learning. This enables us to successfully optimize for multi-criteria path-finding and incorporate representation learning within our framework. Our agent learns to pick favorable streets to create a safe and short path with a reward function that incorporates safety and efficiency. Given access to recent crime reports in many urban cities, we train our model for experiments in Boston, New York, and San Francisco. We test our model on areas of these cities, specifically the populated downtown regions where tourists and those unfamiliar with the streets walk. We evaluate SafeRoute and successfully improve over state-of-the-art methods by up to 17 average distance from crimes while decreasing path length by up to 7


page 7

page 8


AI Safety Gridworlds

We present a suite of reinforcement learning environments illustrating v...

L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation

This work presents a deep reinforcement learning framework for interacti...

Self-supervised learning unveils change in urban housing from street-level images

Cities around the world face a critical shortage of affordable and decen...

Urban Visual Intelligence: Studying Cities with AI and Street-level Imagery

The visual dimension of cities has been a fundamental subject in urban s...

Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability

Sustainable roofs, such as those with greenery and photovoltaic panels, ...

A shape-based heuristic for the detection of urban block artifacts in street networks

Street networks are ubiquitous components of cities, guiding their devel...

Directional Measurements in Urban Street Canyons from Macro Rooftop Sites at 28 GHz for 90

Path gain and effective directional gain in urban canyons from actual ro...

Please sign up or login with your details

Forgot password? Click here to reset