Car automation, navigation and driving assistance are one of the most active various in automobil... more Car automation, navigation and driving assistance are one of the most active various in automobile researches. Recently NASA push for self driving Rover and Google's car project has pushed the limits of the automatic vehicle navigation systems. However, the response of vehicles in real time has been found to be poor due to conventional work flow of microcontroller based processing. With the advancement of IoT and improvement in Internet speed has enabled it to take the decision system to the core and the fogs from the edge devices. Even though there are have been several past works which are proposed effective technology for automated navigational support combined with obstacle avoidance and lane tracking, most of such systems, have been proved to be unrealistic for realistic driving. In this work, we try to solve the problem of computational and navigational latency suffered by existing systems by introducing a combined edge and fog based decision system for the vehicles. We have simulated and tested the proposed work using a custom designed remote control car powered by Intel Edison IoT device and with MQTT as the gateway protocol. Results show that the proposed system has exceedingly quick response over conventional systems. The proposed work combines Image Processing and Machine learning technology efficiently for the decision support system of the car. This results in more efficient and powerful architecture for automated and self driving car.
Car automation, navigation and driving assistance are one of the most active various in automobil... more Car automation, navigation and driving assistance are one of the most active various in automobile researches. Recently NASA push for self driving Rover and Google's car project has pushed the limits of the automatic vehicle navigation systems. However, the response of vehicles in real time has been found to be poor due to conventional work flow of microcontroller based processing. With the advancement of IoT and improvement in Internet speed has enabled it to take the decision system to the core and the fogs from the edge devices. Even though there are have been several past works which are proposed effective technology for automated navigational support combined with obstacle avoidance and lane tracking, most of such systems, have been proved to be unrealistic for realistic driving. In this work, we try to solve the problem of computational and navigational latency suffered by existing systems by introducing a combined edge and fog based decision system for the vehicles. We have simulated and tested the proposed work using a custom designed remote control car powered by Intel Edison IoT device and with MQTT as the gateway protocol. Results show that the proposed system has exceedingly quick response over conventional systems. The proposed work combines Image Processing and Machine learning technology efficiently for the decision support system of the car. This results in more efficient and powerful architecture for automated and self driving car.
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Papers by H. Preeti