Abstract
In passing maneuvers on two-lane highways, assessing the needed distance and the potential power reserve to ensure the required speed mode of the passing vehicle is a critical task of speed planning. This task must meet several mutually exclusive conditions that lead to successful maneuvers. This paper addresses three main aspects. First, the issues associated with a rational distribution of the speed of the passing vehicle for overtaking a long commercial vehicle on two-lane highways are discussed. The factors that affect the maneuver effectiveness are analyzed, considering the safety and cost. Second, a heuristic algorithm is proposed based on the rationale for choosing the necessary space and time for overtaking. The initial prediction’s sensitivity to fluctuations of the current measurements of the position and speed of the overtaking participants is examined. Third, an optimization technique for the passing vehicle speed distribution during the overtaking time using the finite element method is presented. Adaptive model predictive control is applied for tracking the references being generated. The presented model is illustrated using a simulation.
Highlights
The concept of autonomous vehicles (AVs) has been under development since the 1990s [1,2], when the first field experiment performed on a freeway was conducted in San Diego, California
The work initially focused on curvilinear road profiles for which the optimal trajectory of motion was determined using nonlinear model predictive control (MPC), which allowed a consideration of turns and avoidance of moving and static obstacles
This paper presents a new technique of speed planning for the overtaking of autonomous vehicles on two-lane highways
Summary
The concept of autonomous vehicles (AVs) has been under development since the 1990s [1,2], when the first field experiment performed on a freeway was conducted in San Diego, California. Numerous researchers have addressed the issues of planning motion reference lines and control parameters for autonomous vehicles and attempted to find the best trajectories, speed plans, and state-space sequences. The work initially focused on curvilinear road profiles for which the optimal trajectory of motion was determined using nonlinear model predictive control (MPC), which allowed a consideration of turns and avoidance of moving and static obstacles Both kinematic and dynamic models were used as vehicle models. The reason for implementing this approach is that if overtaking is performed with the maximum vehicle performance, there will be a risk of losing lateral stability (due to random external forces) and energy consumption will be high This scenario provides the minimum time and reduces the probability of a head-on collision. If overtaking is performed slowly, a good stability and controllability will be ensured, but there may not be enough of a safety margin at the end of the maneuver
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