Deep Learning Techniques for Obstacle Detection and Avoidance in Driverless Cars

Somayeh Nosrati

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Abstract:

With the advent of the Internet of Things (IoT), The realization of the smart city seems to be very imminent. One of the key parts of a cyber-physical system of urban life is transportation. This mission-critical application has attracted many researchers in both academia and industry to investigate driverless cars. In the domain of autonomous vehicles, intelligent video analytics is very critical. With the advent of deep learning, many neural networks based learning approaches are under consideration. This work tries to implement obstacle detection and avoidance in a Driverless Car. One of advanced neural network called Convolutional Neural Network (CNN) is exploited for real-time video/image analysis using an IoT on Driverless Cars. This project makes use of a raspberry pi which is responsible for controlling the car and performing inference using CNN, based on its current input. The model trained has achieved an accuracy of 88.6% and are in good consent with expected performance.

IEEE Keywords

Automobiles,
Autonomous automobiles,
Convolution,
Artificial intelligence,
Convolutional neural networks

Author Keywords

Internet of Things,
Autonomous car,
Deep Learning,
Convolutional Neural Network,
Raspberry PI

Introduction

Great men said that it is better to prevent and prepare than to repent and repair. The current automobile safety techniques like ABS and airbags have reached a plateau. The technology is progressing and so is the mindset, the aim is not to survive a car accident but is to prevent it from happening. the world has come down such that we believe in the machines we make more than ourselves and this machine can prevent millions of deaths due to accidents and it would be called the self-driving car…

Result

A miniature version of a self-driving car has been implemented, which is capable of decision making with the help of Convolutional Neural Networks. The car was trained on a self-made track which was around 12 to 14 meters long. During the training phase, the car had achieved an accuracy of 88.6%, which can be improved further by hyperparameter tunning and more data collection. The car functioned very well for most for its journey but had difficulties when taking sharp turns, which was due to the limitation of the motor’s turning capabilities. The car had successfully mimicked the driving patterns within the scope of the dataset and would require more data accumulation for adjusting to new scenarios.

Conclusion

Artificial intelligence (deep learning) is one technology that would revolutionize the world as we know it and lead us headfirst into the future. One of the applications of AI and a tranche of Computer vision is the autonomous Driverless Cars. This robotic car might be today’s luxury but is surely tomorrow’s need. In this paper, we have presented a monocular vision-based self-driving car, in which an image input is taken through the camera, and a CNN model is used to take a decision accordingly. For a driverless car, Accuracy and efficiency are directly proportionate to the amount of training data and the quality of the object detection model. This concept calls for an advanced and safe future for all citizens in the field of transportation due to its ability to relay passengers or goods without human intervention.

About KSRA

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