Browse Topic: Cruise control
This paper presents a comprehensive decision-making algorithm for highway overtaking maneuvers, one of the highest risk maneuvers. For such, an overtaking scenario is divided into four phases: approach phase in which the host or overtaking vehicle (HV) detects a slow-moving lead vehicle (LV) in the same lane; left lane change and passing phase in which the HV performs a left lane change and passes the LV; right lane change phase in which the HV comes back to its original lane and free-flowing phase in which the HV maintains its lane and the initially set velocity. Depending on the phase-wise safety zones, the decision-making algorithm makes two decisions: change lane (1 = left lane change, 0 = maintain the same lane, -1 = right lane change) and adjust speed (1 = accelerate, 0 = maintain the current speed, -1 = decelerate). The proposed decision-making algorithm complements the human driver’s decision-making process and can be easily adapted for individual users. Safety and comfort constraints used for defining safety zones and reference trajectories are verified using the highD dataset, consisting of naturalistic vehicle trajectories on German highways. The decision-making algorithm is coupled with a trajectory planner and an operational controller to develop an automated overtaking system. The developed overtaking system is verified in a virtual environment (a combination of Simcenter Prescan and Matlab) by performing many simulations. The results demonstrate the robustness of the decision-making algorithm.
There are a large number of curves and slopes in the mountainous areas. Unreasonable acceleration and deceleration in these areas will increase the burden of the brake system and the fuel consumption of the vehicle. The main purpose of this paper is to introduce a speed planning and promotion system for commercial vehicles in mountainous areas. The wind, slope, curve, engine brake, and rolling resistances are analyzed to establish the thermal model of the brake system. Based on the thermal model, the safe speed of the brake system is acquired. The maximum safe speed on the turning section is generated by the vehicle dynamic model. And the economic speed is calculated according to the fuel consumption model. The planning speed is provided based on these models. This system can guide the driver to handle the vehicle speed more reasonably. According to the simulation, compared to cruise control, speed planning can save fuel consumption at a mean value of 9.13% in typical mountainous areas. The field test of a typical commercial vehicle shows that this system can increase fuel efficiency by 4.26% compared to an experienced driver during a journey in a mountainous area.
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