Encapsulated trajectory tracking control for autonomous vehicles

The motion control of autonomous vehicles with a modular, service-oriented system architecture poses new challenges, as trajectory-planning and -execution are independent software functions. In this paper, requirements for an encapsulated trajectory tracking control are derived and it’s shown that key differences to conventional vehicles with an integrated system architecture exist, requiring additional attention during controller design. A novel, encapsulated control architecture is presented that incorporates multiple extensions and support functions, fulfilling the derived requirements. It allows the application within the modular architecture without loss of functionality or performance. The controller considers vehicle stability and enables the yaw motion as an independent degree of freedom. The concept is applied and validated within the vehicles of the UNICARagil research project, that feature the previously described system architecture to increase flexibility of application by dynamically interconnecting services based on the current use-case

Comparing Different Levels of Technical Systems for a Modular Safety Approval - Why the State of the Art Does Not Dispense with System Tests Yet

While systems in the automotive industry have become increasingly complex, the related processes require comprehensive testing to be carried out at lower levels of a system. Nevertheless, the final safety validation is still required to be carried out at the system level by automotive standards like ISO 26262. Using its guidelines for the development of automated vehicles and applying them for field operation tests has been proven to be economically unfeasible. The concept of a modular safety approval provides the opportunity to reduce the testing effort after updates and for a broader set of vehicle variants. In this paper, we present insufficiencies that occur on lower levels of hierarchy compared to the system level. Using a completely new approach, we show that errors arise due to faulty decomposition processes wherein, e.g., functions, test scenarios, risks, or requirements of a system are decomposed to the module level. Thus, we identify three main categories of errors: insufficiently functional architectures, performing the wrong tests, and performing the right tests wrongly. We provide more detailed errors and present examples from the research project UNICARagil. Finally, these findings are taken to define rules for the development and testing of modules to dispense with system tests

Systemarchitektur und Fahrmanöver zum sicheren Anhalten modularer automatisierter Fahrzeuge

Maschinelle Systeme übernehmen einen immer größer werdenden Anteil der dynamischen Fahraufgabe automatisierter Fahrzeuge. Funktionale Degradationen können die Fähigkeiten dieser Systeme negativ beeinflussen, sodass sie die Fahraufgabe nicht weiter erfüllen können. In diesen Fällen wird bei höher automatisierten Systemen die Fahraufgabe von einer maschinellen Rückfallebene übernommen. Im Rahmen des Forschungsprojekts UNICARagil wird eine modulare und dienstbasierte funktionale Fahrzeugarchitektur entwickelt, für die in diesem Beitrag die Anforderungen und die Systemarchitektur einer geeigneten funktionalen Rückfallebene vorgestellt werden und der weitere Forschungsbedarf hinsichtlich der erforderlichen Fähigkeiten der Teilfunktionen, ihrer gegenseitigen Abhängigkeiten und der Absicherung der Teil- und Gesamtfunktionen erläutert wird

Macroscopic Safety Requirements for Highly Automated Driving

The common expectation for highly automated vehicles (HAVs) is that an introduction will lead to increased road safety and a reduction in traffic fatalities—at least in relation to the mileage. However, quantizing the safety requirements is still in discussion. This paper analyzes the risk acceptance in other fields and applies the safety level on today’s traffic to derive references for acceptable risks. The focus is on macroscopic safety requirements, meaning accident rates per mileage, and not the behavior in individual driving situations. It was concluded that the acceptable risk varies according to the group involved and with the field share of automated vehicles. Increased safety of conventional driving in the future could lead to higher requirements as well. We also point out that it is not guaranteed that the given acceptable risk levels will also accepted by the user, because factors other than the accident statistics are relevant. However, as none of these risk levels can be proven before introduction, the monitoring of vehicles in the field is suggested. Despite increased research efforts in safety validation, uncertainty surrounding the safety of HAVs will remain at the time of introduction. Different introduction and risk management strategies are briefly introduced

Approach to Maintain a Safe State of an Automated Vehicle in Case of Unsafe Desired Behavior

For automated driving, higher levels of automation pose new challenges in terms of safety. In this paper, we develop a generic behavior safety framework that maintains a safe vehicle state even in case of system failures. It is applicable to different configurations of automated driving system architectures. We verify the designed generic behavior safety framework by applying it to two different architectures from both projects PRORETA 5 and UNICARagil. The previously defined safety requirements are met with both applications, which indicates that the developed generic safety framework is also valid for other configurations of automated driving systems

Real-Time Pose Graph SLAM based on Radar

This work presents a real-time pose graph based Simultaneous Localization and Mapping (SLAM) system for automotive Radar. The algorithm constructs a map from Radar detections using the Iterative Closest Point (ICP) method to match consecutive scans obtained from a single, front-facing Radar sensor. The algorithm is evaluated on a range of real-world datasets and shows mean translational errors as low as 0.62 m and demonstrates robustness on long tracks. Using a single Radar, our proposed system achieves state-of-the-art performance when compared to other Radar-based SLAM algorithms that use multiple, higher-resolution Radars

Ideal Reference Point in Planning and Control for Automated Car-Like Vehicles

The choice of the reference point in automated vehicles impacts the vehicle's driving behavior. However, this influence is often not considered for planning and control tasks. To find out where the reference point should be located best, we first consider its position to be ideal if the needed lane width on the left and right side of the planned path is equal when cornering with constant curvature. For constantly curved paths we derive the ideal reference point depending on the curvature, using the kinematics of a slip angle free bicycle model. For non-stationary cornering, we analyze different maneuvers and finally, we select the reference point on the front axle. Utilizing this knowledge, the extent of a forward moving vehicle can be reduced to a point model, which does not require the orientation of the vehicle. This enables a simple and still promising approach for collision checking, where the vehicle's needed space is approximated by only one circle around the reference point. Finally, we analyze the influence of the reference point on a lateral feed-forward controller. Thus, we confirm the previously chosen reference point on the front axle for the equally distributed needed lane width and therefore recommend its use

Data-driven Derivation of Requirements for a Lidar Sensor Model

Safety assurance in virtual driving simulation environments requires accurate sensor models. However, generally accepted quality criteria for sensor models do not yet exist. In this work, we investigate the model quality needed for a Lidar sensor model for virtual validation. We seek to answer the question, whether neglecting sensor effects in a simplified sensor model might lead to a measurable difference in performance of the sensor model compared to a real sensor. A data-driven approach has been chosen to identify relevant features for object classification in Lidar pointclouds which need to be accurately represented in simulations. The contribution of our work is two-fold: Firstly, we identify important features for object detection in point clouds from Lidar data. For this, we apply object classification algorithms to pointcloud segments, for which a variety of geometric, stochastic, and sensor-specific features have been calculated. Using filter models, principal component analysis (PCA), and embedded models, each feature is assessed and ranked on an individual basis. Secondly, we derive implications for Lidar sensor models based on our findings. We investigate variations in classification quality by succesively removing groups of features from our feature set. Our results show, that to make sensor models suitable for the validation of object detection algorithms, the accurate representation of simple geometric features in synthetic pointclouds is sufficient in many cases. Our method can also be used to support the derivation of requirements and validation criteria for sensor models

System Identification Method for Brake Particle Emission Measurements of Passenger Car Disc Brakes on a Dynamometer

Besides particulate emissions from engine exhausts, which are already regulated by emission standards, passenger car disc brakes are a source of particulate matter. With the current car fleet it is estimated that up to 21% of the total traffic related PM10 emissions in urban environments originate from brake wear and reduction of brake dust emissions is subject of current research. For the purpose of reducing brake dust emissions by choosing low-emission operating points of the disc brake, the knowledge of the emission behavior depending on brake pressure, wheel speed, temperature and friction history is of interest. According to the current state of research, theoretical white box modeling of the emission behavior is complicated due to the complexity of tribological contact between pad and disc. Thus experimental black box modeling is supposed to describe emission behavior. In order to minimize the influence of disturbances and therefore to improve prediction accuracy of such empirical models, system identification methods based on periodical test signals, such as brake pressure sine, are used for this application. To adopt these test signals, which are established in transfer function measurements, to the application of brake particle measurements and to develop an experimental design, system theoretical quantities, such as cutoff frequency, signal to noise ratio and hysteresis, are determined in dynamometer tests. Therefore measurements of the system’s response to step and sine test signals are analyzed. System identification is executed and the applicability of periodical test signals to brake particle measurements is proven