In this work we propose proof-of-concept methods to detect malfunctions of the braking system in passenger vehicles. In particular, we investigate the problem of detecting deformations of the brake disc based on data recorded by acceleration sensors mounted on the suspension of the vehicle. Our core hypothesis is that these signals contain vibrations caused by brake disc deformation. Since faults of this kind are typically monitored by the driver of the vehicle, the development of automatic fault-detection systems becomes more important with the rise of autonomous driving. In addition, the new brake boosters separate the brake pedal from the hydraulic system which results in less significant effects on the brake pedal force. Our paper offers two important contributions. Firstly, we provide a detailed description of our novel measurement scheme, the type and placement of the used sensors, signal acquisition and data characteristics. Then, in the second part of our paper we detail mathematically justified signal representations and different algorithms to distinguish between deformed and normal brake discs. For the proper understanding of the phenomenon, different brake discs were used with measured runout values. Since, in addition to brake disc deformation, the vibrations recorded by our accelerometers are nonlinearly dependent on a number of factors (such as the velocity, suspension, tire pressure, etc.), data-driven models are considered. Through experiments, we show that the proposed methods can be used to recognize faults in the braking system caused by brake disc deformation.

This paper deals with a special tracking problem when a ground vehicle should be tracked by a multicopter flying ahead of the vehicle. Pre-designed vehicle route is assumed and the UAV stops or slows down at every intersection to react to route changes. After introducing the problem, the methods applied in a real flight demonstration in the Smart City module of ZalaZONE proving ground are presented. Then new methods are introduced to possibly improve performance. The main focus of the article is the evaluation of the stability of the methods and the provision of tuning guidelines. All of the introduced methods is tuned based-on the guidelines considering real ground vehicle test data and the high fidelity simulation of the applied multicopter. The two best methods are compared in detail and guidelines of their applicability are provided.

Forerunner UAV refers to an unmanned aerial vehicle equipped with a downward-looking camera flying in front of the advancing emergency ground vehicles (EGV) to notify the driver about the hidden dangers (e.g., other vehicles). A feasibility demonstration in an urban environment having a multicopter as the forerunner UAV and two cars as the emergency and dangerous ground vehicles was done in ZalaZONE Proving Ground, Hungary. After the description of system hardware and software components, test scenarios, object detection and tracking, the main contribution of the paper is the development and evaluation of encounter risk decision methods. First, the basic collision risk evaluation applied in the demonstration is summarized, then the detailed development of an improved method is presented. It starts with the comparison of different velocity and acceleration estimation methods. Then, vehicle motion prediction is conducted, considering estimated data and its uncertainty. The prediction time horizon is determined based on actual EGV speed and so braking time. If the predicted trajectories intersect, then the EGV driver is notified about the danger. Some special relations between EGV and the other vehicle are also handled. Tuning and comparison of basic and improved methods is done based on real data from the demonstration. The improved method can notify the driver longer, identify special relations between the vehicles and it is adaptive considering actual EGV speed and EGV braking characteristics; therefore, it is selected for future application.

The forerunner UAV means a camera equipped drone flying in front of the advancing first responder units to increase driver situational awareness with an aerial view of the traffic situation and notification about imminent dangers. This article presents the software-in-the-loop (SIL) simulation of the concept including UNREAL4-Carla as the virtual reality environment with a firetruck driven through a game controller, the Matlab simulation of the DJI M600 forerunner hexacopter with UDP communication between firetruck and M600 and the real-time AI processing of synthetic images to detect ground vehicles and pedestrians. The target of SIL development is threefold. First, to test M600 autopilot and Al-based object detection in close to realistic conditions before the real flights. Second, to make an exhaustive feasibility study of the whole forerunner concept with several simulated situations. Third, to generate the required large amount of image data for AI object detection tuning. After introducing all parts of the SIL simulation the article presents an illustrative example evaluating the tracking of the ground vehicle with the M600 and the inference system results.