SmartYatra: a dynamic driver profiling and vehicle condition monitoring safety shield for accident prevention

This paper presents SmartYatra, an intelligent road-safety framework that integrates driver-behaviour profiling and vehicle-condition monitoring to reduce the risk of accidents. Road accidents are a major concern worldwide, claiming countless lives and leaving countless others injured, and a significant number of them are due to unsafe and reckless driving behaviours combined with poor vehicle conditions. This paper introduces an intelligent road-safety system aimed at reducing such road accidents dynamically by profiling the driver’s behaviour and monitoring the vehicle's condition in real time, which are then used to regulate the maximum allowable speeds tailored to the vehicle in use, thereby ensuring safe driving on the roads. The system also provides instant alerts when there are unsafe driving events and generates weekly performance reports that summarise driver behaviour and vehicle-condition scores, thereby promoting safer driving habits and proactive vehicle maintenance. The results obtained from a simulated environment demonstrated the effectiveness of the system in reducing unsafe driving habits and events, as well as in enhancing road-safety metrics. The accuracies of around 90% were achieved in driver profiling, and experiments benchmarked against rule-based telematics and intelligent speed assistance (ISA) show up to a 78% reduction in overspeed exposure for aggressive profiles (≈52% for normal profiles), achieved using comfort-aware advisory limits, with an overall improvement of 17% in unsafe-event detection and 8% in adaptive-speed accuracy compared with these baseline methods; absolute cruising speeds remain unaffected when compliant. It employs a weighted-scoring algorithm that first computes a driver behaviour score (Bd) from sensors and a vehicle condition score (Vc) from simulated diagnostic parameters. These are fused into a safety score (Sc = αBd + βVc), which dynamically determines the maximum safe speed (Ms) for each trip segment. The design is edge-first, offline-capable, and low-bandwidth, enabling real-time advisory control on commodity devices with minimal back-haul. It establishes a foundation for further improvement and development of intelligent transport systems, such as driver fatigue detection and autonomous vehicle compatibility. This system therefore represents a significant step toward minimising fatal accidents on the road and in maximising and fostering a safer driving environment.