An Improved, Autonomous, Multimodal Estimation Algorithm to Estimate Intent of Other Agents on the Road to Identify Most Important Object for Advanced Driver Assistance Systems Applications Using Model-Based Design Methodology

Advanced Driver Assistance Systems (ADAS) are playing a significant role in enhancing driver safety and occupant comfort in modern vehicles. The primary research focus in this domain includes the precise perception of the current state and the prediction of the future states of dynamic agents. To perform these tasks an intelligent agent capable of operating in the stochastic environment is implemented in the form of various ADAS features. A trajectory prediction problem can be defined using either a model-based or data-driven approach. The current article addresses the problem of trajectory prediction in the stochastic environment using a model-based approach with a quintic polynomial as a function approximator to ensure smooth acceleration trajectory for the left and right lane-change maneuvers. The task of trajectory prediction also considers the information about the vehicle dynamics, the concept of Receding Time Horizon (RTH), and the variable curvature model of the road. Further, the task of assessing the intent of other agents is framed as a Markov decision process problem due to uncertainty in the agent’s action. The information about the predicted trajectory and current state of an agent is processed in the state transition probability estimation module to infer information about the stochastic policy of other agents in the environment using a Naïve Bayes classifier algorithm. The decision made by other agents has been propagated further to the Collision Detection Module to find the Most Important Object (MIO) for various ADAS features. In this way, the current article outlines a robust method to predict the intent of non-ego vehicles and identify the MIO on the road for the ego vehicle to implement various ADAS applications. The capability of the proposed algorithm to handle both uncertainties in the environment and in decision-making by other agents has been validated with several simulated driving scenarios.

• Record URL:
  • https://doi.org/10.4271/12-06-01-0005
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/25740741
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Waghchoure, Mayur Rajendra
  • Patel, Kunj Pareshkuma
  • Rachamadugu, Vysali Nikhita
  • Iqbal, Muhammad Hameem Safwat Husain
  • Veepuri, Sai Kamal Sreeja
  • Deshpande, Bhargav Narsinha
  • Dorle, Aniruddha
• Publication Date: 2022-4-21

Language

• English

Media Info

• Media Type: Digital/other
• Features: References;
• Pagination: pp 51-82
• Serial:
  • SAE International Journal of Connected and Automated Vehicles
  • Volume: 6
  • Issue Number: 1
  • Publisher: SAE International
  • ISSN: 2574-0741
  • Serial URL: https://www.sae.org/publications/collections/content/E-JOURNAL-12/

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Driver support systems; Multi-agent systems; Predictive models; Vehicle mix; Vehicle trajectories
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01911593
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 12-06-01-0005
• Files: TRIS, SAE
• Created Date: Mar 11 2024 4:32PM