Implementing AIML at work can help you advance in your career. Read to learn about Balasubramanyam Padmanabha’s journey with Great Learning’s PGP Artificial Intelligence and Machine Learning Course.
Problem Statement recognized at Work:
- Autonomous Park Assist system – SAE Level 3
What Tools and Techniques were applied while solving the problem?
- Tools: Python, CARLA simulator
- Techniques: Neural Networks
What were the Solutions and Recommendations that were derived while solving this problem?
- As a major intelligent vehicle technology, autonomous parking technology can park a car into a parking spot through environmental perception, path planning, and a series of processes. The autonomous parking system is the main product of ADAS.
- Most of the driving functions in autonomous driving are inherited from manual driving actions. The sequence of driving actions is captured through a set of algorithms with artificial intelligence to make the functions sensible. The learning process is an integral component at both localization and maneuvering levels. The learning component was introduced into the system using the driver’s past experiences. The past experiences of the driver are trained using a neural network so that future prediction and decision-making capability will be improved significantly.
- The proposed system integrates the learning components separately for both localization and maneuvering levels using a neural network.
- The possible parking scenarios are modeled using hypotheses as a feed-forward network which is used as a classifier.
- The second network which is associated with the maneuvering component is a Recurrent Neural Network (RNN).
- The solution was unique as it was completely based on Artificial Intelligence.
- This solution helped me earn huge recognition from company leadership.