From Specifications to Behavior: Maneuver Verification in a Semantic State Space

To realize a market entry of autonomous vehicles in the foreseeable future, the behavior planning system will need to abide by the same rules that humans follow. Product liability cannot be enforced without a proper solution to the approval trap. In this paper, we define a semantic abstraction of the continuous space and formalize traffic rules in linear temporal logic (LTL). Sequences in the semantic state space represent maneuvers a high-level planner could choose to execute. We check these maneuvers against the formalized traffic rules using runtime verification. By using the standard model checker NuSMV, we demonstrate the effectiveness of our approach and provide runtime properties for the maneuver verification. We show that high-level behavior can be verified in a semantic state space to fulfill a set of formalized rules, which could serve as a step towards safety of the intended functionality.Comment: Published at IEEE Intelligent Vehicles Symposium (IV), 201

Falsification-Based Robust Adversarial Reinforcement Learning

Reinforcement learning (RL) has achieved tremendous progress in solving various sequential decision-making problems, e.g., control tasks in robotics. However, RL methods often fail to generalize to safety-critical scenarios since policies are overfitted to training environments. Previously, robust adversarial reinforcement learning (RARL) was proposed to train an adversarial network that applies disturbances to a system, which improves robustness in test scenarios. A drawback of neural-network-based adversaries is that integrating system requirements without handcrafting sophisticated reward signals is difficult. Safety falsification methods allow one to find a set of initial conditions as well as an input sequence, such that the system violates a given property formulated in temporal logic. In this paper, we propose falsification-based RARL (FRARL), the first generic framework for integrating temporal-logic falsification in adversarial learning to improve policy robustness. With falsification method, we do not need to construct an extra reward function for the adversary. We evaluate our approach on a braking assistance system and an adaptive cruise control system of autonomous vehicles. Experiments show that policies trained with a falsification-based adversary generalize better and show less violation of the safety specification in test scenarios than the ones trained without an adversary or with an adversarial network.Comment: 11 pages, 3 figure

A validation process for a legal formalization method

peer reviewedThis volume contains the papers presented at LN2FR 2022: The International Workshop on Methodologies for Translating Legal Norms into Formal Representations, held on December 14, 2022 in a hybrid form (in person workshop was held in Saarland University, Saarbrucken) in association with 35th International Conference on Legal Knowledge and Information Systems (JURIX 2022). Using symbolic logic or similar methods of knowledge representation to formalise legal norms is one of the most traditional goals of legal informatics as a scientific discipline. More than mere theoretical value, this approach is also connected to promising real-world applications involving, e.g., the observance of legal norms by highly automated machines or even the (partial) automatisation of legal reasoning, leading to new automated legal services. Albeit the long research tradition on the use of logic to formalise legal norms-be it by using classic logic systems (e.g., first-order logic), be it by attempting to construct a specific system of logic of norms (e.g., deontic logic)-, many challenges involved in the development of an adequate methodology for the formalisation of concrete legal regulations remain unsolved. This includes not only the choice of a sufficiently expressive formal language or model, but also the concrete way through which a legal text formulated in natural language is to be translated into the formal representation. The workshop LN2FR seeked to explore the various challenges connected with the task of using formal languages and models to represent legal norms in a machine-readable manner. We had 13 submissions, which were reviewed by 2 or 3 reviewers. Among these, we selected 11 papers (seven long papers, three short papers, one published paper) for presentation and discussion

Mecanismos normativos para favorecer la formalización de transporte de vehículos menores en el distrito de Nuevo Chimbote, 2022

El objetivo de esta investigación fue establecer los mecanismos normativos que favorecen el proceso de formalización de vehículos menores en el Distrito de Nuevo Chimbote, 2022. La investigación fue de tipo básica, enfoque cuantitativo, nivel descriptivo, no experimental con una muestra de 330 mototaxistas formales del distrito de Nuevo Chimbote, la técnica empleada para recolectar los datos fue la encuesta, el instrumento el cuestionario. Los resultados revelaron que el 52.4% de conductores mototaxistas valoró las normas relacionadas con la regularización del transporte público de mototaxis, en un nivel regular. Asimismo, se describió cada proceso normativo como: el permiso de operación, requisitos de obtención del permiso, concesión de uso de paradero, requisitos de uso de paradero, credencial y requisitos de la credencial del conductor, registro de transportadores, registro de conductores, registro de vehículos, paraderos formales y características de los paraderos. Concluyendo que, las normativas vigentes respecto a la circulación de mototaxistas, no son lo suficientemente claras, explicitas o van en contravía de la realidad social, por ende, el conductor lo percibe como exigua y opta por la informalidad, asimismo los beneficios que ofrece la formalización pueden resultarles poco relevantes, por esos u otros motivos es que valoran las normas como regulares

Methodology for Specifying and Testing Traffic Rule Compliance for Automated Driving

The introduction of highly-automated driving functions promises to increase safety and comfort, but the safety validation remains an unsolved challenge. Here, the requirement is that the introduction does not reduce safety on public roads. This dissertation addresses one major aspect of road safety: traffic rule compliance. Even an automated vehicle must comply with existing traffic rules. The developed method enables automated testing of traffic rule compliance of automated driving functions. In the first part of the thesis, the state of the art for describing and formalizing behavioral rules is analyzed. A special challenge is posed by the different traffic rules depending on the traffic region. With existing approaches, a separate description and formalization of the behavior rules is necessary for each traffic region or even for individual traffic areas. This shows the necessity to develop new approaches for the abstraction and transferability of the behavioral rules in order to reduce the effort of testing and ensuring traffic rule compliance. The rule compliance criteria are to be integrated into the behavior specification within the functional specification. The objective of this thesis is to develop a method to formalize the limits of traffic rule compliance, based on which fail criteria for system testing are defined and applied. For this purpose, existing traffic rules are analyzed as a basis to identify which behavior constraints are imposed by the static traffic environment. Based on this, a semantic description that is transferable between traffic domains and that links the boundaries of traffic rule compliance to the static traffic environment is developed. The method involves deriving behavioral attributes from which the semantic behavior description is constructed. These behavioral attributes construct the behavior space that describes the boundaries of legally allowed behavior. Furthermore, methods for automated derivation of behavioral attributes from high definition maps are developed, thus extracting the behavioral requirement from an operational design domain. It is investigated which functionalities an automated vehicle has to provide to comply with the behavioral attributes. The attributes are then formalized to obtain quantifiable failure criteria of traffic rule compliance that can be used in automated testing. Finally, building on the state of the art, a test strategy for validating traffic rule conformance is presented. The explicit availability of the behavioral limits results in an advantage in the influence analysis of possible parameters for these tests. Finally, the developed method is applied to existing map material and to test drives with an automated vehicle prototype in order to investigate the practical applicability of the approach as well as the resulting gain in knowledge about traffic rule compliance testing. The developed approach allows to derive the behavioral specification with respect to traffic rule conformance as an essential part of the functional specification independent of the application domain. It is proven that the approach is able to test the traffic rule conformance of an automated vehicle in different test scenarios within an application domain. By applying the developed methodology, it was possible to identify defects in the investigated test vehicle with respect to rule understanding and compliance