Driving Feature Extraction and Behavior Classification Using an Autoencoder to Reproduce the Velocity Styles of Experts

By Kyle Sama, Luis Yoichi Morales Saiki, Naoki Akai, Hailong Liu, Eijiro Takeuchi, Kazuya Takeda. 2018

16 citations recorded by the source corpus.

In this paper we present a work on encoding, clustering and modeling expert driver behaviors to be reproduced by an autonomous driving car agent. We collected speed, brake, steering wheel and acceleration data from an expert driving in typical Japanese suburban areas. We grouped together consecutive data in a sliding window as the input to a fully connected deep autoencoder for dimension reduction. We then clustered the encoded driving data into 9 different behaviors. For each behavior class, we modeled the speed using a fit of same clustered data and tested the models in a ROS car simulator. Results show that the models created from the clusters could represent different driving behaviors on various roads. Experimental simulations show that the proposed approach can reproduce speed behavior comparable to that of the expert drivers even in new environments

Subfields: Automotive Engineering, Building and Construction, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Jacob Lambert
2. Alexander Carballo
3. Ekim Yurtsever
4. Kazuya Takeda

In Building and Construction

1. Ekim Yurtsever
2. Jacob Lambert
3. Alexander Carballo
4. Kazuya Takeda

In Computer Vision and Pattern Recognition

1. Ekim Yurtsever
2. Alexander Carballo
3. Jacob Lambert
4. Kazuya Takeda

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “Another autoencoder network was used to extract road-type specific driving features [240].”