Runtime unknown unsafe scenarios identification for SOTIF of autonomous vehicles

Safety is a critical concern for autonomous vehicles (AVs). Current testing approaches face challenges in simultaneously meeting the requirements of being valid, safe, and fast. To address these challenges, the silent testing approach that tests functions or systems in the background without interfering with driving is motivated. Building upon our previous research, this study first extends the method to specifically address the validation of AV perception, utilizing a lane marking detection algorithm (LMDA) as a case study. Second, field experiments were conducted to investigate the method's effectiveness in validating AV systems. For both studies, an architecture for describing the working principle is presented. The efficacy of the method in evaluating the LMDA is demonstrated through the use of adversarial images generated from a dataset. Furthermore, various scenarios involving pedestrians crossing a road under different levels of criticality were constructed to gain practical insights into the method's applicability for AV system validation. The results show that corner cases of the LMDA are successfully identified by the given evaluation metrics. Furthermore, the experiments highlight the benefits of employing multiple virtual instances with different initial states, enabling the expansion of the test space and the discovery of unknown unsafe scenarios, particularly those prone to false-positive objects. The practical implementation and systematic discussion of the method offer a significant contribution to AV safety validation.

Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors

Safety validation of automated driving functions is a major challenge that is partly tackled by means of simulation-based testing. The virtual validation approach always entails the modeling of automotive perception sensors and their environment. In the real world, these sensors are exposed to adverse influences by environmental conditions such as rain, fog, snow, etc. Therefore, such influences need to be reflected in the simulation models. In this publication, a novel data set is introduced and analyzed. This data set contains lidar data with synchronized reference measurements of weather conditions from a stationary long-term experiment. Recorded weather conditions comprise fog, rain, snow, and direct sunlight. The data are analyzed by pairing lidar values, such as the number of detections in the atmosphere, with weather parameters such as rain rate in mm/h. This results in expectation values, which can directly be utilized for stochastic modeling or model calibration and validation. The results show vast differences in the number of atmospheric detections, range distribution, and attenuation between the different sensors of the data set.

Functional decomposition-A contribution to overcome the parameter space explosion during validation of highly automated driving

Objective: Particular testing by functional decomposition of the automated driving function can potentially contribute to reducing the effort of validating highly automated driving functions. In this study, the required size of test suites for scenario-based testing and the potential to reduce it by functional decomposition are quantified for the first time. Methods: The required size of test suites for scenario-based approval of a so-called Autobahn-Chauffeur (SAE Level 3) is analyzed for an exemplary set of scenarios. Based on studies of data from failure analyses in other domains, the possible range for the required test coverage is narrowed down and suitable discretization steps, as well as ranges for the influence parameters, are assumed. Based on those assumptions, the size of the test suites for testing the complete system is quantified. The effects that lead to a reduction in the parameter space for particular testing of the decomposed driving function are analyzed and the potential to reduce the validation effort is estimated by comparing the resulting test suite sizes for both methods. Results: The combination of all effects leads to a reduction in the test suites' size by a factor between 20 and 130, depending on the required test coverage. This means that the size of the required test suite can be reduced by 95-99% by particular testing compared to scenario-based testing of the complete system. Conclusions: The reduction potential is a valuable contribution to overcome the parameter space explosion during the validation of highly automated driving. However, this study is based on assumptions and only a small set of exemplary scenarios. Thus, the findings have to be validated in further studies.

Perception sensor modeling for virtual validation of automated driving

The automotive innovations in the last decades are dominated by new software-based functions. A validation of these functions is always needed for ensuring the safety of the vehicles. XiL-methods are widely used for this purpose. Along with the development of automated driving vehicles, the demand for simulation based validation of such vehicles is rising, as shown by examples. For the simulations, the modeling of the perception sensors plays an essential role. A state-of-the-art survey clarifies, that a dilemma between simulation efficiency and fidelity of the current methods exists, especially when modeling active sensors. A novel concept of grey box modeling promises to solve the dilemma, or at least to defuse it. With a generalized sensor structure and a simplified propagation modeling, the grey box model considers the most important physically based performances of the sensors and their ranges. The basic structure of the modeling is sketched here, and an outlook for future requirements on it is presented.

Towards cooperative guidance and control of highly automated vehicles: H-Mode and Conduct-by-Wire

This article provides a general ergonomic framework of cooperative guidance and control for vehicles with an emphasis on the cooperation between a human and a highly automated vehicle. In the twenty-first century, mobility and automation technologies are increasingly fused. In the sky, highly automated aircraft are flying with a high safety record. On the ground, a variety of driver assistance systems are being developed, and highly automated vehicles with increasingly autonomous capabilities are becoming possible. Human-centred automation has paved the way for a better cooperation between automation and humans. How can these highly automated systems be structured so that they can be easily understood, how will they cooperate with the human? The presented research was conducted using the methods of iterative build-up and refinement of framework by triangulation, i.e. by instantiating and testing the framework with at least two derived concepts and prototypes. This article sketches a general, conceptual ergonomic framework of cooperative guidance and control of highly automated vehicles, two concepts derived from the framework, prototypes and pilot data. Cooperation is exemplified in a list of aspects and related to levels of the driving task. With the concept 'Conduct-by-Wire', cooperation happens mainly on the guidance level, where the driver can delegate manoeuvres to the automation with a specialised manoeuvre interface. With H-Mode, a haptic-multimodal interaction with highly automated vehicles based on the H(orse)-Metaphor, cooperation is mainly done on guidance and control with a haptically active interface. Cooperativeness should be a key aspect for future human-automation systems. Especially for highly automated vehicles, cooperative guidance and control is a research direction with already promising concepts and prototypes that should be further explored. The application of the presented approach is every human-machine system that moves and includes high levels of assistance/automation.

Body mass index in a large cohort of patients assigned to age decades between <20 and ≥80 years: relationship with cardiovascular morbidity and medication

OBJECTIVE

There is an ongoing debate about the relationship between obesity and morbidity in the elderly, the clinical relevance of overweight and obesity in older patients and the need or harms of treatment. The main purpose of our study was to investigate whether a higher BMI is associated with a worse cardiovascular risk in all age groups, especially in the older ones.

SUBJECTS AND DESIGN

We performed a retrospective evaluation of clinical data from 3926 patients who visited a medical outdoor center for diagnostic and/or therapeutic interventions in the period from January 1995 to July 2010. Patients were assigned to eight age groups of one decade from <20 years to ≥80 years.

RESULTS

The Body Mass Index (BMI) of our patients showed a continuous increase with increasing age with peak values in the age decade 61-70 years (26.29 ±4.42 kg/m2). This was paralleled by an increase in cardiovascular events and need for continuous medication, demonstrating peak values in the age decade 61-70 years (22.3% in the female and 24.7% in the male group). In all age decades up to 80 years the BMI values were higher in patients with events compared to those without it. multivariable linear regression analysis - including confounding variables (blood pressure, fasting glucose, HDL-cholesterol, triglycerides, physical activity, smoking) - revealed for all age groups a strong positive relation of BMI and a negative relation of fat free mass (FFM) to the probability for a cardiovascular event and need for medication.

CONCLUSION

In all age groups, the percentage of cardiovascular events was directly correlated with the BMI. Having in mind the transition to an aging society, therapeutic and preventive strategies should, therefore, include weight management strategies also for the elderly.