Development of a Real-Time Trajectory Planner on Autonomous Electric Vehicles Using the Spatial-Temporal Joint Optimization Method

 

 

The purpose of this research is to provide a new model of Autonomous Driving System (ADS) Electric Vehicle that is accurate, safe, and reliable by developing a real-time trajectory planner model using a spatial-temporal joint optimization framework so that the vehicle can model the geometric constraints of the track used to ensure obstacle detection dynamically.

The spatial-temporal joint optimization framework can efficiently generate high-quality trajectories with full-dimensional assurance, complemented by the integration of a Multisensor-aided Inertial Navigation System (MINS) topology to improve the accuracy of sensor fusion (IMU, wheel encoder, camera, GNSS, and LiDAR). Furthermore, the framework is integrated with the lightweight hybrid A* algorithm, which provides the advantage of rapidly generating safe trajectory planning.

Simulation testing of the proposed framework will be conducted in two environments: static traffic environments and dynamic traffic environments. This research is a research and development (R&D) study using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) approach to achieve success.

Documentation:

This research was funded by the Directorate General of Higher Education, Research, and Technology (Ditjen Diktiristek) through the Directorate of Research, Technology, and Community Service (DRTPM). It was also chaired by Professor Dr. Subiyanto, S.T., M.T., from the Department of Electrical Engineering at UNNES.

The research team included lecturers Arimaz Hangga, S.T., M.T., Dr. Eng. Aldias Bahatmaka, and Lambang Setyo Utomo, A.Md.
Assisted by diligent and passionate students from the Department of Electrical Engineering at UNNES, class of 2024: Deyndrawan Sutrisno, Setya Budi Arif Prabowo, Elfandy Yunus, Muhammad Hilmi Farras, and Diadora Sanggrahita.