CARISSMA Institute of Safety in Future Mobility (C-ISAFE)
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Automatizované řízení
(2022)
Automated Valet Parking Systems (AVPS) relieve the driver of the entire parking process. Many of the systems known today rely on a combination of automotive sensors with sensors of the infrastructure. For this purpose, parking facilities are equipped with comprehensive sensor technology to support the vehicles in environment sensing and route planning. This approach is comparatively expensive which is why many parking operators don’t provide that technology to their customers. This paper proposes a lean AVPS system architecture that requires minimal effort to adapt the infrastructure. At the same time, state-of-the-art vehicle technology is used to make AVPS more profitable overall. At the beginning, an overview will be given describing the state of the art of AVPS. Subsequently, requirements for the AVPS will be elaborated, whereby the system can be designed and implemented in the following. Finally, the presentation of simulation results shows that one doesn’t have to extend the infrastructure with sensors to develop a safe and reliable AVPS.
Crash-Prone Fault Combination Identification for Over-Actuated Vehicles During Evasive Maneuvers
(2024)
Throughout a vehicle’s lifecycle, systems may fail during operation, requiring effective fault management by the vehicle controller. Various system faults affect vehicle handling differently. Additionally, vehicle velocity and road friction directly impact handling and stability. Thus, it is essential to investigate relevant factors, such as actuator faults, vehicle velocity, road friction, and their combinations, before developing a fault-tolerant controller to mitigate potential critical situations. Our work thus focuses on identifying faults and fault combinations that might lead to crashes for over-actuated vehicles during evasive maneuvers and those impacting comfort parameters. We employ a state-of-the-art vehicle controller optimized for evasive lane changes for over-actuated vehicles. The driving scenario encompasses critical conditions defined in ISO 26262 with ASIL-D, including velocities up to 130 km/h and requiring steering away from obstacles. Failure Mode and Effects Analysis, Design of Experiments, and statistical tools are used to determine fault combinations most likely to lead to crashes during evasive maneuvers. Our results indicate that the vehicle controller successfully handled the maneuver in over 53% of investigated cases, reaching up to 75.1% on dry surfaces. Road friction emerges as the most critical parameter for collision avoidance and comfort. Brake faults exhibit a higher influence on vehicle handling than other actuator faults, while single motor faults do not significantly impact vehicle parameters. Regarding two-factor interactions, brake actuators dominate, followed by steering and motor. These findings provide valuable insights for developing fault-tolerant controllers for over-actuated vehicles, guiding decisions on addressing specific faults to enhance safety and comfort parameters.
Development of a high-level System Architecture for a cost-effective Automated Valet Parking System
(2023)
Airbag pressure determines the restraint effect during a vehicle crash. The pressure required to restrain the occupant depends on pre-crash detection, collision parameters and the occupant’s mass and position. This work modulated airbag pressure for optimum safety using a novel airbag control valve for cold-gas inflators. This paper evaluates the valve’s stationary and dynamic performances for Helium by 3D flow simulations using a pressure-based solver in ANSYS Fluent® and SAE J2238 laboratory tank tests. The predicted and measured tank pressures for the fully open (stationary) valve were agreed by an average 93.73% with an excellent correlation (correlation coefficient, R = 0.9995). For the first dynamic operation with 10 ms switching time, the results agreed by 92.78% with R = 0.9975. In the second test with 30 ms switching, 83.67% agreement was observed with R = 0.9893. The research concluded that the valve modulates the bag pressure and is implementable in vehicles.
This paper describes the development of an intelligent infrastructure, a test field, for the safety assurance of automated vehicles within the research project Ingolstadt Innovation Laboratory (IN2Lab). It includes a description of the test field architecture, the RoadSide Units (RSU) concept based on infrastructure-based sensors, the environment perception system, and the mission control system. The study also proposes a global object fusion method to fuse objects detected by different RSUs and investigate the overall measurement accuracy obtained from the usage of different infrastructure-based sensors. Furthermore, it presents four use cases: traffic monitoring, assisted perception, collaborative perception, and extended perception. The traffic monitoring, based on the perception information provided by each roadside unit, generates a global fused object list and monitors the state of the traffic participants. The assisted perception, using vehicle-to-infrastructure communication, broadcasts the state information of the traffic participants to the connected vehicles. The collaborative perception creates a global fused object list with the local detections of connected vehicles and the detections provided by the roadside units, making it available for all connected vehicles. Lastly, the extended environment perception monitors specific locations, recognizes critical scenarios involving vulnerable road users and automated vehicles, and generates a suitable avoidance maneuver to avoid or mitigate the occurrence of collisions.
Autonomous driving perception tasks rely heavily on cameras as the primary sensor for Object Detection, Semantic Segmentation, Instance Segmentation, and Object Tracking. However, RGB images captured by cameras lack depth information, which poses a significant challenge in 3D detection tasks. To supplement this missing data, mapping sensors such as LIDAR and RADAR are used for accurate 3D Object Detection. Despite their significant accuracy, the multi-sensor models are expensive and require a high computational demand. In contrast, Monocular 3D Object Detection models are becoming increasingly popular, offering a faster, cheaper, and easier-to-implement solution for 3D detections. This paper introduces a different Multi-Tasking Learning approach called MonoNext that utilizes a spatial grid to map objects in the scene. MonoNext employs a straightforward approach based on the ConvNext network and requires only 3D bounding box annotated data. In our experiments with the KITTI dataset, MonoNext achieved high precision and competitive performance comparable with state-of-the-art approaches. Furthermore, by adding more training data, MonoNext surpassed itself and achieved higher accuracies.
Driver’s Chest Position Detection using FMCW Radar Data Collected in a Vehicle Mock-up and CNN
(2023)
Risk in Stochastic and Robust Model Predictive Path-Following Control for Vehicular Motion Planning
(2023)
The development of finite element vehicle models for crash simulations is a highly complex task. The main aim of these models is to simulate a variety of crash scenarios and assess all the safety systems for their respective performances. These vehicle models possess a substantial amount of data pertaining to the vehicle's geometry, structure, materials, etc., and are used to estimate a large set of system and component level characteristics using crash simulations. It is understood that even the most well-developed simulation models are prone to deviations in estimation when compared to real-world physical test results. This is generally due to our inability to model the chaos and uncertainties introduced in the real world. Such unavoidable deviations render the use of virtual simulations ineffective for the calibration process of the algorithms that activate the restraint systems in the event of a crash (crash-detection algorithm). In the scope of this research, authors hypothesize the possibility of accounting for such variations introduced in the real world by creating a feedback loop between real-world crash tests and crash simulations. To accomplish this, a Reinforcement Learning (RL) compatible virtual surrogate model is used, which is adapted from crash simulation models. Hence, a conceptual methodology is illustrated in this paper for developing an RL-compatible model that can be trained using the results of crash simulations and crash tests. As the calibration of the crash-detection algorithm is fundamentally dependent upon the crash pulses, the scope of the expected output is limited to advancing the ability to estimate crash pulses. Furthermore, the real-time implementation of the methodology is illustrated using an actual vehicle model.
Coupled controllers are vital for safely handling vehicles, especially in critical driving situations that include changing lanes to avoid obstacles. Controllers specialized in emergencies must keep road users safe in critical situations. In this paper, we develop the coupled controller to handle evasive maneuvers for an over-actuated vehicle. The controller is based on the second-order sliding mode control theory. We use the bicycle model to establish the equivalent and robust steering equations as a control-oriented model. The lateral and longitudinal vehicle motions are coupled to each other by the lateral vehicle information on the longitudinal sliding surface, and the dependence of the lateral sliding surface on the longitudinal velocity. The torque vectoring method based on fuzzy logic adjusts the yaw moment. We address the tire slip circle on the slip controller to stabilize the vehicle while maneuvering. We simulate and evaluate our controller in a rear-end collision situation with a short time window to maneuver the vehicle. The ego vehicle detects the preceding vehicle and performs an evasive lane change while simultaneously applying brakes to bring the vehicle to a halt. Our research is the earliest in providing an ultimate emergency control to successfully avoid crashes up to 130 km/h in short time crash detection.
A novel concept for validation of pre-crash perception sensor information using contact sensor
(2020)
The derivation of real accident scenarios from accident databases represents an important task within vehicle safety research. Simulations are increasingly used for this purpose. Depending on the research interest, a wide range of accident databases exists worldwide, which differ mainly in the number of recorded data per accident and availability. This work aims to identify critical vehicle-to-vehicle accidents based on freely available accident databases to derive concrete scenarios for a subsequent simulation. For this purpose, the method of the pre-crash matrix is applied using the example of the freely available Crash Investigation Sampling System database of the National Highway Traffic Safety Administration. An analysis of existing databases worldwide shows that this is the most detailed, freely available database. The derivation of scenarios succeeds here by a new method, whereby a center of gravity calculation is carried out based on the damages of the vehicles according to Collision Deformation Classification nomenclature. In addition, the determination of other necessary parameters, as well as the limits of the database, is shown in order to derive a scenario that can be simulated. As a result, the constellations of the five most frequent vehicle-to-vehicle accident scenarios according to the Crash Investigation Sampling System database are presented. In particular, other institutions should follow National Highway Traffic Safety Administration’s example and make data freely available for accident research.
Verification of Crash Severity Estimation Algorithm for Autonomous Driving Using Real Crash Data
(2021)
The relevance of scientific investigations, whether simulative or empirical, is strongly related to the environment used and the scenarios associated with it. Within the field of cooperative intelligent transport systems, use-cases are defined to describe the benefits of applications. This has already been conducted in the available safety-relevant Day 1 applications longitudinal and intersection collision risk warning through the respective technical specifications. However, the relevance of traffic scenarios is always a function of accident severity and frequency of a retrospective consideration of accident databases. In this study, vehicle-to-vehicle scenarios with high frequency and/or severe personal injuries are therefore determined with the help of the CISS database and linked to the use-cases of the safety-relevant Day 1 applications. The relevance of the scenarios thus results on the one hand from the classical parameters of retrospective accident analysis and on the other hand from the coverage by the named vehicle-to-x applications. As a result, accident scenarios with oncoming vehicles are the most relevant scenarios for investigations with cooperative intelligent transport systems. In addition, high coverage of the most critical scenarios within the use-cases of longitudinal and intersection collision risk warning is already apparent.
Concept of a Vehicle Platform for Development and Testing of Low-Speed Automated Driving Functions
(2021)
The automated driving requires new testing approaches, which are more complex than the current testing systems. The complexity and requirements for accuracy is important, because of interconnection of virtual with physical testing. This paper presents a generic approach to testing of automated driving functions and demonstrates its implementation on measurement of two scenarios.
Activation time for activating the occupant restraint systems (airbag and seatbelt) is very critical for an optimal safety action. A crash-pulse is the deceleration of the vehicle measured during in a crash. The shape, slope, maximum deceleration and duration of the crash-pulse provides significant information over the nature of occupant motions during in-crash phase and hence the crash severity. The above parameters of the crash-pulse not only depend on the mass and impact velocity but also on the crash configuration (position of impact, overlap, relative approach angle etc.).
This study focuses on analysis and characterization of crash-pulses in head-on collision cases with varying overlap configurations. The paper describes causes for occupant injuries during a crash, crash-pulse and its important physical parameters, and different methodologies used to analyse the crash-pulse. Finite element simulation method is used to study the crash-pulses from different crash configurations. A new severity index that has direct influence on the occupant kinematics is defined. The results show that the steep decrease of crash-pulse for small overlap configurations (less than 25 percent of vehicle width) lags by 20 to 25 milliseconds as compared to configurations with large overlaps. The shape of the crash-pulse also changes for crash scenarios with different overlap configurations. The results, discussion and conclusion sections of this paper provide a summary of crash behaviour of varying overlap crash scenarios and insights that can be used for deployment of restraint systems.
In the automotive industry, sensors and sensor systems are one of the most important components in upcoming challenges like highly automated and autonomous driving. Forward-looking sensors (radar, lidar and cameras) have the technical capability to already provide important (pre-)crash information, such as the position of contact, relative crash velocity and overlap (width of contact) before the crash occurs. Future safety systems can improve crash mitigation with sophisticated vehicle safety strategies based on this information. One such strategy is an early activation of restraint systems compared with conventional passive safety systems. These integrated safety systems consist of a combination of predictive forward-looking sensors and occupant restraint systems (airbags, belt tensioners, etc.) to provide the best occupant safety in inevitable crash situations. The activation of the restraint systems is the most critical decision process and requires a very robust validation system to avoid false activation. Hence, the information provided by the forward-looking sensor needs to be highly reliable. A validation sensor is required to check the plausibility of crucial information from forward-looking sensors used in integrated safety systems for safe automated and autonomous driving.
This work presents a CFRP-based (carbon-fiber-reinforced plastic) validation sensor working on the principle of change in electrical resistance when a contact occurs. This sensor detects the first contact, gives information on impact position (where the contact occurs) and provides information on the overlap. The aim is to activate the vehicle restraint systems at near T0 (time of first contact). Prototypes of the sensor were manufactured in house and manually and were evaluated. At first, the sensor and its working principle were tested with a pendulum apparatus. In the next stage, the sensor was tested in a real crash test. The comparison of the signals from the CFRP-based sensor with presently used crash sensors in the vehicle highlights its advantages. The crash event can be identified at 0.1 ms after the initial contact. The sensor also provides information on impact position at 1.2 ms and enables a validation of the overlap development. Finally, a possible algorithm for the vehicle safety system using forward-looking sensors with a validation sensor is described.
Automotive airbags protect occupants from crash forces during severe vehicle collisions. They absorb energy and restrain the occupants by providing a soft cushion effect known as the restraint effect. Modern airbags offer partial restraint effect control by controlling the bag’s vent holes and providing multi-stage deployment. Full restraint effect control is still a challenge because the closed-loop restraint control system needs airbag–occupant contact and interaction feedback.
In this work, we have developed novel single and matrix capacitive tactile sensors to measure the occupant’s contact data. They can be integrated with the airbag surface and folded to follow the dynamic airbag shape during the deployment. The sensors are tested under a low-velocity pendulum impact and benchmarked with high-speed test videos. The results reveal that the single sensor can successfully measure occupant–airbag contact time and estimate the area, while the contact position is additionally identified from the matrix sensor.