Comparison of random forest and long short-term memory network performances in classification tasks using radar | IEEE Conference Publication | IEEE Xplore

Comparison of random forest and long short-term memory network performances in classification tasks using radar


Abstract:

Robust semantic knowledge of the environment is one of the building blocks for autonomous driving. If different sensor types are employed for the same task independently,...Show More

Abstract:

Robust semantic knowledge of the environment is one of the building blocks for autonomous driving. If different sensor types are employed for the same task independently, the overall accuracy and safety of the system can increase. Therefore, it is desirable to maximize each sensor's capabilities and to build up redundancies, as it is often required by functional safety. To this end, this paper demonstrates how classification of dynamic objects using solely radar sensors can be performed. Two different methods are utilized and compared: a random forest classifier and a long short-term memory network (LSTM).
Date of Conference: 10-12 October 2017
Date Added to IEEE Xplore: 04 December 2017
ISBN Information:
Conference Location: Bonn, Germany

References

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