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A Generalised Method for Adaptive Longitudinal Control Using Reinforcement Learning

  • Shashank PathakEmail author
  • Suvam Bag
  • Vijay Nadkarni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

Adaptive cruise control (ACC) seeks intelligent and adaptive methods for longitudinal control of the cars. Since more than a decade, high-end cars have been equipped with ACC typically through carefully designed model-based controllers. Unlike the traditional ACC, we propose a reinforcement learning based approach – RL-ACC. We present the RL-ACC and its experimental results from the automotive-grade car simulators. Thus, we obtain a controller which requires minimal domain knowledge, is intuitive in its design, can accommodate uncertainties, can mimic human-like behaviour and may enable human-trust in the automated system. All these aspects are crucial for a fully autonomous car and we believe reinforcement learning based ACC is a step towards that direction.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Visteon Electronics GmbHAn der RaumFabrik 33bKarlsruheGermany
  2. 2.Visteon CorporationSanta ClaraUSA

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