Introduction of Traffic Situation Management for a Rigid Truck, Tests Conducted on Object Avoidance by Steering within Ego Lane

Awareness in traffic situations and manoeuvring efficiently through complex scenarios is a critical task to be managed for active safety and autonomous vehicle applications. This task could be assigned as a separate functionality layer due to its complexity. A reference development framework for autonomous heavy vehicle applications called Traffic Situation Management functionality layer is presented in this study. This functionality layer is then verified by developing a real time autonomous rear end collision avoidance function by steering based on the reference framework. The motion of the truck is restricted within the existing lane, representing situations where there is a partial lateral interference by other vehicles, with safe distance to manoeuvre both in the longitudinal and lateral directions. Lane markings are used as a reference to guide the vehicle within the ego lane during the avoidance manoeuvre. Based on the traffic scenario and ego vehicle states an escape path is generated. A simple feed-forward and PD based feedback controller is used to track the generated path. Physical tests are conducted on a 6X2 rigid heavy truck to verify the proposed function. Results indicate satisfactory performance of the avoidance function and safe margins during the test runs.