Development and implementation of an autonomous vehicle prototype with ADAS

Road traffic accidents are a significant global issue, resulting in considerable fatalities and injuries each year. The United Nations aims to decrease such incidences by 50% by 2030. Advanced Driver Assistance Systems (ADAS) provide a revolutionary solution; yet, their implementation is predominantly restricted to high-priced electric vehicles, limiting their influence to affluent markets. This study advocates for a paradigm shift by showcasing the viability of an economical autonomous vehicle prototype integrated with ADAS capabilities, created using cost-efficient and readily available technology. The prototype used a Raspberry Pi as the core computing unit, incorporating ultrasonic sensors for obstacle detection, a GPS module for navigation, and a camera for traffic signal recognition. These components, while more basic and far less costly than those in premium automobiles, effectively provide essential functions such as automated braking, GPS navigation, and adherence to traffic signals. The project employs an Agile approach, enabling iterative development and swift responsiveness to input. This research demonstrates the potential of democratising ADAS technology to provide safer, smarter automobiles to under-represented socioeconomic groups. Future developments could enhance the system’s precision by integrating advanced sensors, such as LiDAR and radar, while maintaining cost-effectiveness, fostering inclusive innovation in vehicle safety.