In this paper, we propose a driving assistant companion system that provides drivers with useful information using a long short-Term memory network in a data-driven fashion. Our system can be viewed as an advanced driver-Assistance system for faster learning and better driving. The assistant companion predicts upcoming events on the road by using real-Time sensory measurements from range finding sensors and provides informative narrations to enhance learnability and driving performance. In contrast to a conventional navigation system, our system predicts events from the online stream of sensory measurements without resorting to priors and map information. To demonstrate the effectiveness of the proposed system, we implemented our system on The Open Racing Car Simulator and conducted an experimental study with 16 human drivers. Experimental results show that our system enhanced learnability, driving performance, and reliability.
Bibliographical noteFunding Information:
Manuscript received September 6, 2017; revised January 6, 2018; accepted July 12, 2018. Date of publication July 31, 2018; date of current version January 3, 2019. This work was supported by the Ministry of Trade, Industry and Energy (MOTIE) and the Korea Institute for the Advancement of Technology (KIAT) (N0002385, 2017). Paper no. TII-17-2095. (Corresponding author: Jongeun Choi.) The authors are with the School of Mechanical Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org; jongeunchoi@ yonsei.ac.kr).
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Electrical and Electronic Engineering