  Automated Parking Assist (APA) systems are now facing great challenges of low
adoption in applications, due to users' concerns about parking capability,
reliability, and completion efficiency. To upgrade the conventional APA
planners and enhance user's acceptance, this research proposes an
optimal-control-based parking motion planner. Its highlight lies in its control
logic: planning trajectories by mirroring the parking target. This method
enables: i) parking capability in narrow spaces; ii) better parking reliability
by expanding Operation Design Domain (ODD); iii) faster completion of parking
process; iv) enhanced computational efficiency; v) universal to all types of
parking. A comprehensive evaluation is conducted. Results demonstrate the
proposed planner does enhance parking success rate by 40.6%, improve parking
completion efficiency by 18.0%, and expand ODD by 86.1%. It shows its
superiority in difficult parking cases, such as the parallel parking scenario
and narrow spaces. Moreover, the average computation time of the proposed
planner is 74 milliseconds. Results indicate that the proposed planner is ready
for real-time commercial applications.
