Summary of Designing Deep Neural Networks For Driver Intention Recognition, by Koen Vellenga et al.
Designing deep neural networks for driver intention recognition
by Koen Vellenga, H. Joe Steinhauer, Alexander Karlsson, Göran Falkman, Asli Rhodin, Ashok Koppisetty
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates how different neural network architectures affect driver intention recognition, a crucial application for safety-critical systems with limited computational capabilities. To tackle this problem, the authors employ neural architecture search to explore three types of layers capable of handling sequential data: long-short term memory (LSTM), temporal convolutional layer (TCL), and time-series transformer layer (TSL). The study evaluates eight search strategies on two datasets, discovering that no single strategy consistently outperforms others. However, the authors find that performing architecture search improves model performance compared to manual design. Surprisingly, increasing model complexity does not lead to better driver intention recognition. Instead, multiple architectures achieve similar results regardless of layer type or fusion strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how different brain-inspired computer networks affect our ability to understand what drivers are thinking and planning. It’s like trying to figure out what someone is going to do next based on their past actions. The researchers tried lots of different ways to build these networks, using three special types that can handle data that changes over time. They tested eight different methods on two big collections of data and found that no one method was clearly the best. However, they did find that letting the computer figure out its own network architecture made it perform better than if a person had designed it manually. What’s really interesting is that making the network more complicated didn’t necessarily make it better at understanding driver intentions. |
Keywords
* Artificial intelligence * Lstm * Neural network * Time series * Transformer
