A Receding Horizon Autopilot for the Two-Lane Highway Automated Driving Application through Synergy between the Robust Behavior Planner and the Advanced Driver Assistance Features

Safety is always a crucial aspect of developing autonomous systems, and the motivation behind this project comes from the need to address the traffic crashes occurring globally on a daily basis. The present work studies the coexistence of the novel rule-based behavioral planning framework with the five key advanced driver assistance system (ADAS) features as proposed in this article to fulfill the safety requirements and enhance the comfort of the driver/passengers to achieve a receding-horizon autopilot. This architecture utilizes data from the sensor fusion and the prediction module for the prediction time horizon of 2 s iteratively, which is continuously moving forward (hence, the receding horizon), and helps the behavior planner understand the intent of other vehicles on the road in advance. Further, that information helps the behavior planner make an appropriate decision regarding the activation of specific ADAS features to drive safely on the highway, and that decision is being updated with every iteration or after 0.01 s. The driver assistance features are well equipped to deal with any eventuality on the road with proper guidance from the behavior planner

Currently, there exist local, global, and behavior planners for planning the target trajectory of the ego vehicle (the vehicle that is comprised of the sensors that perceive the environment around it and which needs to operate with the intended level of autonomy) in order to deliver a safe and comfortable drive. Here, the goal of the system is for each ADAS feature to act independently and their synergy with the behavior planner leads to automated driving on the two-lane highway without the need for a global planner to guide it toward the goal. A finite-state machine consisting of a state flow model is used to switch between various driving modes based on the information from the behavior planner and the autopilot models. The behavioral planning framework incorporates a cost function library to determine the best set of ADAS features for the ego vehicle based on the lowest cost and its interaction with other actors in a complex and stochastic environment. The cost function-based algorithm ensures that the vehicle follows the traffic rules, safety, and comfort criteria without compromising performance and thus increases the robustness of the ADAS features leading to the autopilot capabilities for the two-lane one-way highway driving applications. The functionality of the behavior planner framework has been validated by incorporating the model-in-loop (MIL) testing method. The automated driving toolbox in MATLAB was used to perform MIL testing by creating an appropriate number of scenarios.