Bridging the Sim-to-Real Gap in Motion Planning for Autonomous Electric Vehicles Using Autoware: A Comparative Study of Simulation and Real-World Deployment | Zendy

Manikandan Ganesan | Zendy; Bharatiraja Chokkalingam | Zendy; Sivanathan Kandhasamy | Zendy; Rajesh Verma | Zendy; Lucian Mihet-Popa | Zendy

Open Access

Bridging the Sim-to-Real Gap in Motion Planning for Autonomous Electric Vehicles Using Autoware: A Comparative Study of Simulation and Real-World Deployment

Author(s) -

Manikandan Ganesan,

Bharatiraja Chokkalingam,

Sivanathan Kandhasamy,

Rajesh Verma,

Lucian Mihet-Popa

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3587991

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Road traffic accidents result in over 1.35 million fatalities annually, highlighting the urgent need for safer transportation systems. Autonomous Electric Vehicles (AEVs), powered by advancements in Intelligent Transportation Systems (ITS), offer a promising solution by enhancing road safety and efficiency. This study focuses on trajectory planning for AEVs, a critical component for safe navigation in dynamic environments. Using Autoware.ai, an open-source modular platform, we developed a novel interface integrating custom point cloud (PCD) and vector maps for precise localization and navigation. The proposed method was tested in both simulation and real-world scenarios on the SRM IST campus, addressing challenges such as the “sim-to-real gap” caused by discrepancies between simulated and real-world conditions. Quantitative analysis of planned, simulated, and measured trajectories revealed significant improvements in localization accuracy and trajectory adherence, with Root Mean Square Error (RMSE) values for longitudinal and lateral positions reduced to 0.839 m and 0.1044 m, respectively. Velocity and steering angle tracking demonstrated minor deviations due to actuator constraints and road conditions. This research provides valuable insights into bridging the sim-to-real gap in AEV trajectory planning, paving the way for safer and more reliable autonomous driving systems.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research