Model predictive control for energy-efficient and safe driving on curved freeways under black ice surface conditions

Driving on curved roads under black ice can be difficult and risky due to a lack of visibility, unanticipated loss of traction, and the curvature effect. This greatly impacts driving behavior and often requires drivers to brake and accelerate, causing extra energy consumption and skidding. To address this issue, in this paper, we propose an energy-efficient and safe (eco-safe) driving strategy (EDS) for a host car employing nonlinear model predictive control (NMPC) that incorporates information on road curvatures and black ice surface conditions. An objective function is formulated considering parameters that affect fuel economy and driving safety, and solved using a nonlinearly constrained optimization technique with a finite prediction horizon. The EDS produces optimum acceleration and velocity trajectories for the host car by utilizing its motion dynamical model, preceding vehicle states, and information on road curvatures and icy surface conditions. Microscopic traffic simulations evaluate the performance of the EDS on typical freeways. Numerical results demonstrate that the proposed NMPC-based EDS significantly mitigates the host car’s fuel usage and emissions compared to the conventional driving strategy (CDS) whilst maintaining dynamic safety gaps. Specifically, for transition curves and S-curves, the proposed EDS enhances fuel efficiency by 6.76% and 7.82%, respectively, while reducing emissions (CO 2 , HC, CO, and NO x ) by 6.54%–21.06% and 7.29%–34.97%, respectively. Likewise, the time to collision (TTC) with the proposed EDS remains below 2.5 s for both icy roadways, assuring safety. Moreover, the proposed EDS reduces the host car’s braking force, thus enhancing skidding safety by reducing skidding risk on icy surfaces. The proposed system is deployable as an advanced driving assistance system (ADAS) for semi-autonomous mobility. Finally, we recommend a pertinent policy for the proposed system, specific to these critical road conditions.