Vehicle safety depends on (a) the range of identified hazards and (b) the operational situations for which mitigations of these hazards are acceptably decreasing risk. Moreover, with an increasing degree of autonomy, risk ownership is likely to increase for vendors towards regulatory certification. Hence, highly automated vehicles have to be equipped with verified controllers capable of reliably identifying and mitigating hazards in all possible operational situations. To this end, available methods for the design and verification of automated vehicle controllers have to be supported by models for hazard analysis and mitigation.
Babin et al. @cite_13 propose a system reconfiguration approach developed with the Event-B method in a correct-by-construction fashion using a behavior pattern similar to our approach (particularly, fig:statemodel ). Reconfiguration as one way to faults is discussed in this work. Wardzi ' n ski @cite_1 discusses hazard identification and mitigation for autonomous vehicles by predetermined risk assessment ( with safety barriers) and dynamic risk assessment. For both, he provides argumentation patterns for creating AV safety cases. In addition to his work, the abstraction and the method we propose covers both paradigms in one framework. We provide formal notions of all core concepts.
@cite_1 @cite_13
2024131347 2898045695 2294668865 2138997468
In this paper, we present a hierarchical smart resource coordination and reconfiguration framework for distributed systems. We view the coordination problem as one of context aware resource reconfiguration. The fundamental unit in this hierarchy is a Fault Containment Unit (FCU) that provides run-time fault-tolerance by deciding on the best alternative course of action when a failure occurs. FCUs are composed hierarchically and are responsible for dynamically reconfiguring failing FCUs at lower levels. When such a reconfiguration is not possible, FCUs propagate the failure upward for resolution. We evaluate the effectiveness of our framework in a people tracking application using a network of cameras. The task for our multi-camera network is to allocate pairs of cameras that localize a subject optimally given the current run-time context. The system automatically derives policies for switching between camera pairs that enable robust tracking while being attentive to certain performance measures. Our approach is unique in that we model the dynamics in the scene and the camera network configuration steers the policies to provide robust tracking. Many current autonomous systems are being designed with a strong reliance on black box predictions from deep neural networks (DNNs). However, DNNs tend to be overconfident in predictions on unseen data and can give unpredictable results for far-from-distribution test data. The importance of predictions that are robust to this distributional shift is evident for safety-critical applications, such as collision avoidance around pedestrians. Measures of model uncertainty can be used to identify unseen data, but the state-of-the-art extraction methods such as Bayesian neural networks are mostly intractable to compute. This paper uses MC-Dropout and Bootstrapping to give computationally tractable and parallelizable uncertainty estimates. The methods are embedded in a Safe Reinforcement Learning framework to form uncertainty-aware navigation around pedestrians. The result is a collision avoidance policy that and cautiously avoids pedestrians that exhibit unseen behavior. The policy is demonstrated in simulation to be more robust to novel observations and take safer actions than an uncertainty-unaware baseline. We consider planning problems, that often arise in autonomous driving applications, in which an agent should decide on immediate actions so as to optimize a long term objective. For example, when a car tries to merge in a roundabout it should decide on an immediate acceleration braking command, while the long term effect of the command is the success failure of the merge. Such problems are characterized by continuous state and action spaces, and by interaction with multiple agents, whose behavior can be adversarial. We argue that dual versions of the MDP framework (that depend on the value function and the @math function) are problematic for autonomous driving applications due to the non Markovian of the natural state space representation, and due to the continuous state and action spaces. We propose to tackle the planning task by decomposing the problem into two phases: First, we apply supervised learning for predicting the near future based on the present. We require that the predictor will be differentiable with respect to the representation of the present. Second, we model a full trajectory of the agent using a recurrent neural network, where unexplained factors are modeled as (additive) input nodes. This allows us to solve the long-term planning problem using supervised learning techniques and direct optimization over the recurrent neural network. Our approach enables us to learn robust policies by incorporating adversarial elements to the environment. Risk estimation for the current traffic situation is crucial for safe autonomous driving systems. One part of the uncertainty in risk estimation is the behavior of the surrounding traffic participants. In this paper we focus on highway scenarios, where possible behaviors consist of a change in acceleration and lane change maneuvers. We present a novel approach for the recognition of lane change intentions of traffic participants. Our novel approach is an extension of the Naive Bayesian approach and results in a generative model. It builds on the relations to the directly surrounding vehicles and to the static traffic environment. We obtain the conditional probabilities of all relevant features using Gaussian mixtures with a flexible number of components. We systematically reduce the number of features by selecting the most powerful ones. Furthermore we investigate the predictive power of each feature with respect to the time before a lane change event. In a large scale experiment on real world data with over 160.781 samples collected on a test drive of 1100km we trained and validated our intention prediction model and achieved a significant improvement in the recognition performance of lane change intentions compared to current state of the art methods.