Model-Based Systems Engineering Applied to the Trajectory Planning for Autonomous Vehicles

Passing maneuver is a complex driving maneuver critical to success of autonomous vehicles. It becomes more challenging in two-way roads. In this study, a passing scenario with three vehicles is considered where car 1, an autonomous vehicle (AV), is moving behind car 2 (human-driven) in the same lane and car 3 (human-driven) is part of the oncoming traffic in the adjacent lane. The primary goal is to develop a framework to analyze measurement-based decision-making strategies for the AV satisfying driving safety constraints and while considering collaboration amongst drivers. The problem is structured using SysML to build a modular architecture with clearly defined interfaces and allocated behaviors. Mathematical models have been modeled in MATLAB. The integrated model of the car passing problem was then executed in MATLAB to study the effect of collaboration on safety of car 1 if car 1 decides to pass. It was observed that the controller of car 1 made the correct decision whether to execute the passing maneuver or not, based on the feasibility of the maneuver. When car 1 decided to pass, the collaboration between the autonomous vehicle and the humandriven vehicles reduced the crashes if car 1's decision was erroneous due to measurement noise. The work demonstrates how a Model-Based Systems Engineering approach can be used in the context of the car passing problem to manage complexity, developing virtual prototypes for analysis and deriving design requirements for autonomous vehicles.