SafeRoute AI: A Comprehensive Review of Safety-Aware Intelligent Navigation Systems

Abstract: Modern navigation systems are predominantly designed to optimize travel efficiency—minimizing distance, time, or fuel consumption—while treating personal safety as a secondary consideration. This gap is particularly significant in dense urban environments where crime rates and environmental hazards are geographically distributed in ways that conventional routing algorithms cannot account for. SafeRoute AI proposes an integrated architecture that embeds a machine learning risk prediction layer directly into the navigation decision pipeline. The system draws from structured crime statistics, geographic coordinates, and temporal signals to generate per-location risk estimates, subsequently applying a parameterized cost function that balances spatial distance against predicted danger. Route computation employs a modified A* algorithm in which a beta-weighted safety cost (β = 0.7) takes precedence over an alpha-weighted distance term (α = 0.3), ensuring that computed paths prioritize safety over raw travel efficiency. This review surveys existing literature on intelligent navigation and safety-aware systems, proposes a four-tier taxonomy classifying systems by depth of safety integration, analyzes critical research gaps in the current body of work, and outlines the complete SafeRoute AI system architecture. The analysis demonstrates that safety-aware navigation constitutes an underdeveloped yet tractable engineering challenge well- suited to current machine learning tooling, with significant potential to improve quality of life for users navigating high-risk urban environments.

Keywords: Safety-Aware Navigation; Risk Prediction; XGBoost; A* Algorithm; Crime Mapping; Intelligent Routing; Emergency Alert; Route Optimization; Machine Learning; Geographic Information Systems