Advancing Safety in Cooperative Vehicle Platooning Through IoT Connectivity and Convolutional Neural Networks

Future transportation systems might benefit greatly from cooperative vehicle platooning, in which vehicles move near one another to decrease air resistance and increase fuel efficiency. However, ensuring everyone in these platoons stays safe is their primary priority. Combining the power of convolutional neural networks (CNNs) and the connection offered by the Internet of Things (IoT), this paper proposes a method to improve the security of cooperative vehicle platooning. Firstly, the interoperability provided by the IoT makes it possible for vehicles to transmit important data such as location, speed, and sensor readings in real time. This makes it easier for platoon members to work together and initiates preventative safety actions. The second use of CNNs is in advanced vision tasks, which include lane tracking, object identification, and anomaly detection. CNNs can alert platoon drivers and the autonomous driving system to possible dangers and course deviations by evaluating data from various onboard sensors, including lidar and cameras. A hierarchical safety design that combines conventional safety features is adaptive cruise control and emergency braking systems with connected IoT devices and perception based on CNNs. Simulated outcomes show that the proposed method significantly improves responsiveness to dangerous situations and decreases accident rates within cooperative vehicle platoons to increase safety. Challenges include providing dependable communication, managing the complexity of sensor data, attaining real-time processing, and mitigating safety issues in dynamic conditions.