Design of a vehicle navigation system integrating CART decision tree and DBSCAN algorithm

Introduction Accurate road type recognition and pavement information aggregation are essential for reliable vehicle navigation in complex urban conditions. Methods This paper integrates a CART decision tree with DBSCAN clustering within an edge-cloud architecture. CART classifies roads using 28-dimensional sensor features via Gini gain optimization and post-pruning. DBSCAN (Eps = 30 m, MinPts = 3) aggregates and denoises pavement information points using Haversine distance. Results CART achieved 96.8% accuracy, 97.2% precision, 97.3% recall, and a 97.2% F1 score, outperforming RF, SVM, and LR with minimal model size and inference time. DBSCAN merged 85.7% of redundant points with noise below 5%. Real-vehicle tests showed 98% + recognition accuracy for non-ordinary urban roads, a 92.5% detection rate, 89.2% warning success rate, and a 126 m average advance warning distance. Discussion The CART-DBSCAN integration provides an effective closed-loop navigation solution from perception to warning. Future work will incorporate multi-modal sensors and domain adaptation to enhance robustness across diverse scenarios.