Rising labour costs, but not least the increasingly evident shortage of available skilled workers – including drivers – have for some time now been the key drivers behind the further development of autonomous, driverless operations in public transport.
A few years ago, between 2019 and 2022, Siemens Mobility carried out extensive practical tests on the automation of (shunting) processes at the Potsdam transport authority’s depot using a retrofitted older Combino tramcar. The Potsdam trials showed that depot automation can significantly improve operational precision, optimise vehicle readiness, and reduce manual workload for depot staff. For Siemens, this was one of the key elements of a wider digitalization and automation process in public transport.
Czech manufacturer Škoda is on that pathway, too, and wants to take things a step further. In Mannheim, Škoda Group and local public transport operator Rhein-Neckar-Verkehr GmbH (rnv) presented a broad vision for the future of intelligent tram depots and autonomous urban rail transport today, on 27 May 2026. The “Autonomy Day” demonstration focused on how digitalisation, automation, artificial intelligence, and smart operational systems can transform tram operations, maintenance, and safety. The event combined practical demonstrations inside a working depot with live tram operations on the Mannheim urban network, showing how future tram systems could operate with significantly less manual intervention while improving efficiency and safety.
Škoda’s approach to a Smart Depot
A central feature of the presentation was the Smart Depot concept, which Škoda Group considers a crucial step toward fully autonomous tram systems operating in city streets. The Smart Depot is designed as a highly interconnected technological environment that combines autonomous vehicle movement, digital operational management, and automated servicing activities. Instead of treating automation as a single technology, the concept integrates many individual systems into one coordinated operational ecosystem.
Within the depot environment, trams demonstrated autonomous movements such as automatic parking, controlled vehicle routing, obstacle handling, and automated passage through washing facilities. These movements were supervised through a central control system capable of coordinating vehicle operations across the depot. The approach allows operators to optimise depot workflows, reduce repetitive manual tasks, and improve the efficiency of maintenance and fleet deployment.
Škoda Group emphasised that the Smart Depot also functions as a testing ground for future autonomous mobility. Technologies can be validated in a controlled depot environment before being introduced onto urban streets. The company highlighted that technical development must progress alongside regulatory development, as future autonomous public transport systems will require new safety standards, operational regulations, and legal frameworks. For this reason, Škoda Group is participating in broader industry discussions concerning the future regulation of autonomous urban mobility.
Anti-Collision System ACS
Another major topic during the event was operational safety. Škoda introduced its ACS (Anti-Collision System) and ISL (Intelligent Speed Limiter) technologies, both intended to support drivers and reduce risks in complex urban traffic conditions. ACS continuously analyses the tram’s trajectory and surrounding environment to detect possible obstacles and dangerous situations. Unlike simpler warning systems, ACS focuses specifically on the vehicle’s actual driving corridor, helping reduce unnecessary false alarms that can distract drivers in dense city environments.
During the demonstrations, participants experienced simulations of emergency situations, including automatic braking scenarios during regular tram operations in Mannheim. The ACS system can intervene when necessary to mitigate or prevent collisions. The technology also creates a digital twin of the transport environment, generating operational data and risk heat maps. These analytical tools may later help transport operators identify hazardous locations, improve traffic planning, and implement targeted safety measures throughout the tram network.
The ISL system complements ACS by managing vehicle speed according to infrastructure conditions and vehicle localisation. It can automatically support speed control in curves or on unfamiliar routes and can react if the driver becomes inattentive or incapacitated. Together, ACS and ISL represent a major step toward safer semi-autonomous and eventually autonomous tram operation.
Digital fleet management
Digital fleet management formed another important part of the presentation. Škoda showcased Mobility Apps designed for operational control, diagnostics, maintenance planning, and dispatching. These applications enable transport operators to monitor and manage entire tram fleets from a central control room. Fleet managers can track vehicle availability, coordinate depot movements, and evaluate operational performance using real-time data and key performance indicators.
One of the most advanced aspects of these applications is predictive maintenance. Using digital twins, artificial intelligence, and continuous data collection from vehicles and infrastructure, the system can identify early signs of component wear or failure before a breakdown occurs. This Condition-Based Maintenance approach allows maintenance to be scheduled according to the actual condition of the tram rather than fixed service intervals. As a result, operators can reduce unexpected failures, increase vehicle availability, and lower maintenance costs.
Škoda also presented AI-supported visual inspection technologies. Cameras and image analysis systems automatically inspect critical tram components such as pantographs, brakes, and exterior structures. The system can detect damage, deformations, or abnormal operating conditions and combine visual data with operational diagnostics to provide more accurate maintenance assessments.
Another innovation highlighted during the event was Self-Surveillance technology. Unlike traditional passive CCTV systems, this solution uses AI-powered real-time analysis of the tram’s surroundings and interior spaces. Cameras can detect people or objects in dangerous areas around the vehicle, monitor spaces between coupled vehicles, and support safer departures from stops or depots. The system can automatically warn drivers, dispatchers, or operational staff about emerging risks.
Self-Surveillance also improves passenger safety and comfort. It can help identify vandalism, monitor unusual situations inside the tram, and provide additional situational awareness during operations. In critical cases, information can be shared with control centres or emergency authorities.
The way to go
The developments presented by Skoda and rnv in Mannheim reflect a broader European trend toward intelligent, automated urban mobility systems. Automatization and digitalization of all types of operational processes are becoming the practical and essential foundation for the future introduction of autonomous tram systems in European cities.
