IEEE Access (Jan 2023)

Architecture and Decision-Making for Autonomous Tram Development

  • Khansa Salsabila Suhaimi,
  • Abdurraafi' Syauqy,
  • Mohammad Salman Subki,
  • Bambang Riyanto Trilaksono,
  • Arief Syaichu Rohman,
  • Yulyan Wahyu Hadi,
  • Handoko Supeno,
  • Dhimas Bintang Kusumawardhana,
  • Dewi Nala Husna

DOI
https://doi.org/10.1109/ACCESS.2023.3293659
Journal volume & issue
Vol. 11
pp. 71714 – 71726

Abstract

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A decision-making system is an essential component of an autonomous tram. The decision-making system uses information on the surrounding area to recognize the traffic situation and determine appropriate actions to maintain safety and passenger comfort. However, guaranteeing reliable performance and safety remains challenging in all driving situations. The development of autonomous trams involves iterative engineering-related tasks. Therefore, proper architecture to facilitate the flexibility of engineering development is required. This paper proposes a modular architecture for a decision-making system for autonomous trams. The decision-making system can serve as a high-level controller for autonomous trams. The architecture consists of risk assessment and decision & planning modules. It also integrates several key functions, such as trajectory prediction, safety assessment, adaptive cruise control (ACC), collision avoidance (CA), and emergency braking system (EBS). In the decision and planning module, a finite-state machine is devised as part of the decision-making system. This module provides a speed reference for low-level speed controllers. In addition, the decision-making system architecture was implemented and its implementation was validated using a Carla simulator. Under a mixed-traffic scenario, simulation results showed that the decision-making system has a high percentage of mission successes. Out of 50 simulations in mixed-traffic scenarios, the tram reached its destination with an 80% success rate in which the success rates of ACC, CA, and EBS executions to avoid collisions were 96.94%, 100%, and 100%, respectively. In addition, the system works well in real-time to recognize the surrounding environment and determine actions. Although beyond the scope of the decision-making system, the simulation results also indicate that an improving performance of the tram’s low-level speed controller may provide more reliable performance of the decision-making system.

Keywords