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.
