A longitudinal driver model for long horizon speed prediction in powertrain applications Two major emerging fields of research in automotive engineering are autonomous driving and electromobility. The predictive or intelligent longitudinal control within the former and consumption forecasts for the latter are dependent on lookahead data provided by cloud based services as real time road and traffic data. Furthermore, these applications can be improved by customization to the driver. This paper proposes a simple, yet accurate parametric model for longitudinal driving characteristics which is designed for use in powertrain applications that could be a predictive, intelligent cruise controller or a personalized consumption forecast. A methodology for offline identification of the model parameters is presented that can be easily transferred to online implementation. The model is validated against measurement data and meaningful metrics for assessing its performance are introduced. It is shown that predicted speed yields good resemblance to measurements.

Modeling, Learning and Prediction of Longitudinal Behaviors of HumanDriven Vehicles by Incorporating Internal Human DecisionMaking Process using Inverse Model Predictive Control Understanding the behaviors of humandriven vehicles such as acceleration and braking are critical for the safety of the nearfuture mixed transportation systems which involve both automated and humandriven vehicles. Existing approaches in modeling human driving behaviors including drivermodelbased 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.