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This study presents a radar‐based predictive kinetic energy management (PKEM) framework that is applicable as an add‐on driver assistance module for a heavy vehicle with an internal combustion engine powertrain. The proposed framework attempts to minimise fuel consumption by estimating the motion of the leading vehicle from radar information and optimising the inputs to the ego vehicle in a predictive manner. The PKEM framework consists of a driver‐pedal pre‐filter, an interacting multiple model radar‐based filter and predictor of traffic object states, and a non‐linear model predictive controller. The framework is integrated with established human‐driver car‐following models representing various driving styles and evaluated over a set of standardised driving cycles. The authors found that the energy‐saving benefits can be as much as 23% over the baseline driver‐only case with minimal compromises on travel time in urban environments, while the benefits are nearly negligible on the highway cycle. The results included also show the potential trade‐offs in accommodating driver‐desired inputs.

An autonomous driving control system that incorporates notions from human-like social driving could facilitate an efficient integration of hybrid traffic where fully autonomous vehicles (AVs) and human operated vehicles (HOVs) are expected to coexist. This paper aims to develop such an autonomous vehicle control model using the social-force concepts, which was originally formulated for modeling the motion of pedestrians in crowds. In this paper, the social force concept is adapted to vehicular traffic where constituent navigation forces are defined as a target force, object forces, and lane forces. Then, nonlinear model predictive control (NMPC) scheme is formulated to mimic the predictive planning behavior of social human drivers where they are considered to optimize the total social force they perceive. The performance of the proposed social force-based autonomous driving control scheme is demonstrated via simulations of an ego-vehicle in multi-lane road scenarios. From adaptive cruise control (ACC) to smooth lane-changing behaviors, the proposed model provided a flexible yet efficient driving control enabling a safe navigation in various situations while maintaining reasonable vehicle dynamics.

Vehicular communication technology (V2V, V2I, V2X, etc.) allows participating vehicles to actively exchange information about their surrounding traffic. By sharing the local field of view (FoV) perception along with their own instantaneous states, communicating vehicles can cooperatively broaden and improve their perceptions of the overall traffic which allows more effective maneuver planning and vehicle energy management. This paper outlines a framework for decentralized multi-vehicle cooperative perception where each vehicle broadcasts its perception information and also acts as an individual fusing node for the received data. The Bhattacharyya distance filter (BDF) is applied for the data association process, identifying and grouping a set of estimates potentially representing the same vehicle. The associated estimates are then passed down to a covariance intersection-based fusion scheme that yields a single fused state estimate and covariance matrix for each vehicle. A metric is adopted to assess the performance of the cooperative perception scheme. The workings of the proposed framework and its potential advantages over other solutions is illustrated via simulations of connected vehicles in highway traffic scenarios with different vehicle densities.

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