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Abstract This paper investigates temporal correlations in human driving behavior using real-world driving to improve speed forecasting accuracy. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discount factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control system, and its influence is analyzed on the prediction accuracy and the performance of the ecological adaptive cruise control in an electric vehicle powertrain model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4.7% compared to a baseline least-square-based polynomial regression. This is a 10% improvement over the constant speed/acceleration model, a conventional speed predictor. 

While perturbation schemes for vehicle-to-vehicle (V2V) communications can address data privacy concerns, they can significantly compromise the performance of the speed controllers of connected automated vehicles (CAVs) if such controllers rely on the preview information available through V2V in car-following scenarios. This paper presents a robust predictive speed controller for a CAV when preview information is provided through a privacy-guaranteed V2V communication network. This is the first such controller that considers energy and emissions concurrently. The impact of privacy assurance in the communication data is studied, while inter-vehicular distance constraint is guaranteed to be satisfied through a robust design of the predictive controller using a robust control invariant set. The robust optimal speed controller is shown to reduce fuel consumption and emissions successfully while satisfying the constraints even in the presence of perturbations in the V2V communication. Results suggest a need for an integrated design procedure to achieve the best performance under a given level of privacy guarantee and emissions requirements. 

Machine learning models developed from real-world data can inherit potential, preexisting bias in the dataset. When these models are used to inform decisions involving human beings, fairness concerns inevitably arise. Imposing certain fairness constraints in the training of models can be effective only if appropriate criteria are applied. However, a fairness criterion can be defined/assessed only when the interaction between the decisions and the underlying population is well understood. We introduce two feedback models describing how people react when receiving machine-aided decisions and illustrate that some commonly used fairness criteria can end with undesirable consequences while reinforcing discrimination.