Introduction of Traffic Situation Management for a rigid truck, tests conducted on object avoidance by steering within ego lane

Paper in proceeding, 2015

Awareness in traffic situations and manoeuvering 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. The functionality layer is then verified by developing a real time rear end collision avoidance function by steering. The motion of the truck is restricted within the existing lanes representing situations where there is a partial lateral interference by other vehicles, with safe distance to manoeuvre 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.