Designing deep neural networks for driver intention recognition
Koen Vellenga H. Joe Steinhauer Alexander Karlsson Göran Falkman Asli Rhodin Ashok Koppisetty
Driver intention recognition studies increasingly rely on deep neural
networks. Deep neural networks have achieved top performance for many different
tasks, but it is not a common practice to explicitly analyse the complexity and
performance of the network's architecture. Therefore, this paper applies neural
architecture search to investigate the effects of the deep neural network
architecture on a real-world safety critical application with limited
computational capabilities. We explore a pre-defined search space for three
deep neural network layer types that are capable to handle sequential data (a
long-short term memory, temporal convolution, and a time-series transformer
layer), and the influence of different data fusion strategies on the driver
intention recognition performance. A set of eight search strategies are
evaluated for two driver intention recognition datasets. For the two datasets,
we observed that there is no search strategy clearly sampling better deep
neural network architectures. However, performing an architecture search does
improve the model performance compared to the original manually designed
networks. Furthermore, we observe no relation between increased model
complexity and higher driver intention recognition performance. The result
indicate that multiple architectures yield similar performance, regardless of
the deep neural network layer type or fusion strategy.