Automobile sector is growing every day with fast affinity towards Autonomous vehicles. The most challenging task of ADAS based driverless car is to identify and track the objects in front of the vehicle. To implement this type of technology we require a robust algorithm which can classify the object just-in-time and have great accuracy.
We are using automotive radar sensor of 77GHz frequency. Quite often we’ve noticed sudden fluctuations in prediction of the obstacles using either heuristic or even machine learning techniques which focus only on frame-wise / cycle-wise data. So, this inspires us to investigate the history of the data coming in as opposed to only one cycle at a time. Hence, we incorporated a technique wherein we could make use of the past data as well as current cycle data.
In this paper, we’ve used Radar time series data to classify the object in front of the Ego vehicle in each Radar cycle. The time series data collected from RADAR enables the reliable prediction of object to be an obstacle or not. Various Radar parameters are collected, analyzed, and fed into LSTM network, capable of handling order dependence, to predict whether the object in front is an obstacle or not. This will help in EBA and ACC functionality to avoid collisions and hazardous situations.