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This paper presents the ACC system for autonomous trams, including three core processes: object detection, collision assessment and control. The system utilizes the YOLOX deep learning model for camera-based object detection and a clustering approach for LiDAR point clouds, integrating both outputs through a late fusion method for enhanced accuracy. The positions of objects and the tram are projected onto a data map to assess collision risk. This data map, which is a virtual 2D space configured with the track’s UTM coordinates and ROI lines, is used to determine whether objects are located within the ROI. The ACC system adjusts the tram’s speed to control acceleration or deceleration based on the relative distances of objects within the ROI. Experiments on a test track demonstrate effective collision avoidance and reliable ACC system operation under predefined scenarios.

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.

The objective of this paper is to examine the types of autonomous vehicles already employed in transportation. It exemplifies the segments of transportation which already use autonomous vehicles. This paper gives an insight of the places in the world where autonomous vehicles are put in use. Furthermore, companies such as Volvo, Volkswagen, Airbus and WAYMO have showed their models of driverless vehicles ranging from trucks, buses, shuttles and helicopters. Moreover, the paper provides a description, testing methods, use and features of the each vehicle presented. In short, this paper aims to raise consciousness about the future of not just public transportation but also transportation of any other type such as good's transportation, delivery, vehicles provided for sightseeing, aircrafts etc.