Automated vehicle (AV) minibuses, i.e., AV shuttles, are gaining popularity in the testing of new types of transportation services in real traffic conditions. AV shuttles have moved from closed test areas to low-traffic public sites such as local residential areas, technology parks, university campuses, etc. These types of vehicles are usually low-speed and rely on a lidar-camera sensor set and a self-driving software stack. These new use cases are increasing these systems’ safety demands. In addition to functional safety, many other aspects need to be considered. In this study, a risk analysis model is developed, combining the fuzzy analytical hierarchy process and the Technique for Order of Preference by Similarity to Ideal Solution method. The proposed model is utilized to prioritize risks corresponding to the particular case study, based on real AV shuttle bus development, and focuses on the low-level hardware/software safety issues and improvements.

The rapid development of intelligent control technology has affected the logistics industry and led to the implementation of new concepts of autonomous indoor logistic robots. These robots are operating inside warehouses and factories to move goods and packages around in the rooms. There is a high motivation to reduce the cost of these mobile robots as well as to create flexible control systems. Tallinn University of Technology in cooperation with a Kulinaaria production plant has developed a new small scale logistic robot to move boxes in rather tight spaces. New technology, including AI and machine vision, is applied to achieve the flexibility and cost-effectiveness of the newly developed mobile robot solution.

This paper presents an approach to transfer the whole set of low-level control systems from one robot bus i.e. autonomous shuttle to another one that has different specifications in electronics and mechanical perspectives. In this work, we executed a series of experiments to test the reliability and safety of the autonomous shuttle after transferring the critical control systems related to the steering and brakes into the shuttle. To fulfill the requirements to register an autonomous shuttle as a legal vehicle on the road in Estonia, we carried out several vital tests of the shuttle’s low-level control system. For example, we manually disconnected the different subsystems to simulate the sudden failure to check whether the shuttle acts with the corresponding protocols (i.e., when the steering Control Area Network fails, the shuttle should initiate the braking and cut off the high-voltage power). This paper proves the possibility of transferring the low-level control systems between the different models of the autonomous shuttle without risking encountering safety/reliability-related issues. Our open-source solution will be helpful for the practical promotion and commercialization of autonomous shuttles in the future.

The safety of autonomous vehicles depends not only on perception and planning, but also on the correctness of low-level electronic signals that connect controllers and actuators. Errors at this interface, caused by hardware degradation, timing violations, software faults, or unexpected interactions, can lead to unsafe behavior even when high-level autonomy functions operate correctly. Existing safety mechanisms primarily focus on system behavior, trajectories, or controller design, leaving actuator-bound command streams largely unmonitored. This paper proposes a low-level, AI-enabled anomaly-detection layer for autonomous vehicle architectures. The core idea is to embed a lightweight observer within a virtualized master controller to monitor control-signal streams in real time without interfering with the primary control logic. The proposed framework combines a stacked LSTM sequence classifier with rule-based safety constraints and context-aware monitoring to detect physically implausible or temporally inconsistent command behavior before actuation. A proof-of-concept simulation study was conducted to evaluate the practicality of the approach using overtaking scenarios in a co-simulated high-level and low-level environment. The results show that the proposed concept can identify severe abnormal low-level behavior and provide preliminary warning/error indications, supporting its potential as a complementary safety layer at the control-to-actuation interface.