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Modeling, Learning and Prediction of Longitudinal Behaviors of Human-Driven Vehicles by Incorporating Internal Human DecisionMaking Process using Inverse Model Predictive Control | IEEE Conference Publication | IEEE Xplore

Modeling, Learning and Prediction of Longitudinal Behaviors of Human-Driven Vehicles by Incorporating Internal Human DecisionMaking Process using Inverse Model Predictive Control


Abstract:

Understanding the behaviors of human-driven vehicles such as acceleration and braking are critical for the safety of the near-future mixed transportation systems which in...Show More

Abstract:

Understanding the behaviors of human-driven vehicles such as acceleration and braking are critical for the safety of the near-future mixed transportation systems which involve both automated and human-driven vehicles. Existing approaches in modeling human driving behaviors including driver-model-based approaches and heuristic approaches have issues in either model accuracy or scalability limitation to new situations. To address these issues, this paper proposes a new inverse model predictive control (IMPC) based approach to model longitudinal human driving behaviors. The approach incorporates the internal decision making process of humans, and achieves better predicting accuracy and improved scalability to different situations. The modeling, learning, and prediction of longitudinal human driving behaviors using the proposed IMPC approach are presented. Experimental results validate the effectiveness and advantages of the approach.
Date of Conference: 03-08 November 2019
Date Added to IEEE Xplore: 28 January 2020
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Conference Location: Macau, China

References

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