Restart-Based Fault-Tolerance: System Design and Schedulability Analysis

Embedded systems in safety-critical environments are continuously required to deliver more performance and functionality, while expected to provide verified safety guarantees. Nonetheless, platform-wide software verification (required for safety) is often expensive. Therefore, design methods that enable utilization of components such as real-time operating systems (RTOS), without requiring their correctness to guarantee safety, is necessary. In this paper, we propose a design approach to deploy safe-by-design embedded systems. To attain this goal, we rely on a small core of verified software to handle faults in applications and RTOS and recover from them while ensuring that timing constraints of safety-critical tasks are always satisfied. Faults are detected by monitoring the application timing and fault-recovery is achieved via full platform restart and software reload, enabled by the short restart time of embedded systems. Schedulability analysis is used to ensure that the timing constraints of critical plant control tasks are always satisfied in spite of faults and consequent restarts. We derive schedulability results for four restart-tolerant task models. We use a simulator to evaluate and compare the performance of the considered scheduling models

Residual Policy Learning for Vehicle Control of Autonomous Racing Cars

The development of vehicle controllers for autonomous racing is challenging because racing cars operate at their physical driving limit. Prompted by the demand for improved performance, autonomous racing research has seen the proliferation of machine learning-based controllers. While these approaches show competitive performance, their practical applicability is often limited. Residual policy learning promises to mitigate this by combining classical controllers with learned residual controllers. The critical advantage of residual controllers is their high adaptability parallel to the classical controller's stable behavior. We propose a residual vehicle controller for autonomous racing cars that learns to amend a classical controller for the path-following of racing lines. In an extensive study, performance gains of our approach are evaluated for a simulated car of the F1TENTH autonomous racing series. The evaluation for twelve replicated real-world racetracks shows that the residual controller reduces lap times by an average of 4.55 % compared to a classical controller and zero-shot generalizes to new racetracks.Comment: Submitted to IEEE Intelligent Vehicles Symposium 202

Propulsion System Testing for a Long-Endurance Solar-Powered Unmanned Aircraft

The increase in popularity of unmanned aerial vehicles (UAVs) has been driven by their use in civilian, education, government, and military applications. However, limited on-board energy storage significantly limits flight time and ultimately usability. The propulsion system plays a critical part in the overall energy consumption of the UAV; therefore, it is necessary to determine the most optimal combination of possible propulsion system components for a given mission profile, i.e. propellers, motors, and electronic speed controllers (ESC). Hundreds of options are available for the different components with little performance specifications available for most of them. In order to determine the performance specifications, a propulsion system testing apparatus has been developed and validated. This testing apparatus was designed to measure the performance and efficiency parameters of electric propulsion system components (propellers, motors, and ESC) while maintaining similar air flow characteristics in either a wind tunnel or on a moving automotive platform. Validation tests of four propellers are presented. All four propellers were tested under static conditions, and two were tested under advancing flow conditions where the testing apparatus was used on an automotive platform. The results show that this propulsion testing system provides for holistic testing of all possible compatible electric propulsion system components in a flight-like environment. Data from this system will be used in a mission-based propulsion system optimizer, currently in development, to select the best combination of components for a long-endurance solar-powered unmanned aircraft

WCET Derivation under Single Core Equivalence with Explicit Memory Budget Assignment

In the last decade there has been a steady uptrend in the popularity of embedded multi-core platforms. This represents a turning point in the theory and implementation of real-time systems. From a real-time standpoint, however, the extensive sharing of hardware resources (e.g. caches, DRAM subsystem, I/O channels) represents a major source of unpredictability. Budget-based memory regulation (throttling) has been extensively studied to enforce a strict partitioning of the DRAM subsystem’s bandwidth. The common approach to analyze a task under memory bandwidth regulation is to consider the budget of the core where the task is executing, and assume the worst-case about the remaining cores' budgets. In this work, we propose a novel analysis strategy to derive the WCET of a task under memory bandwidth regulation that takes into account the exact distribution of memory budgets to cores. In this sense, the proposed analysis represents a generalization of approaches that consider (i) even budget distribution across cores; and (ii) uneven but unknown (except for the core under analysis) budget assignment. By exploiting the additional piece of information, we show that it is possible to derive a more accurate WCET estimation. Our evaluations highlight that the proposed technique can reduce overestimation by 30% in average, and up to 60%, compared to the state of the art.Accepted manuscrip

UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach

Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods. We propose a new end-to-end reinforcement learning (RL) approach to UAV-enabled data collection from Internet of Things (IoT) devices in an urban environment. An autonomous drone is tasked with gathering data from distributed sensor nodes subject to limited flying time and obstacle avoidance. While previous approaches, learning and non-learning based, must perform expensive recomputations or relearn a behavior when important scenario parameters such as the number of sensors, sensor positions, or maximum flying time, change, we train a double deep Q-network (DDQN) with combined experience replay to learn a UAV control policy that generalizes over changing scenario parameters. By exploiting a multi-layer map of the environment fed through convolutional network layers to the agent, we show that our proposed network architecture enables the agent to make movement decisions for a variety of scenario parameters that balance the data collection goal with flight time efficiency and safety constraints. Considerable advantages in learning efficiency from using a map centered on the UAV's position over a non-centered map are also illustrated.Comment: Code available under https://github.com/hbayerlein/uav_data_harvesting, IEEE Global Communications Conference (GLOBECOM) 202

Fixed-Priority Memory-Centric Scheduler for COTS-Based Multiprocessors

Memory-centric scheduling attempts to guarantee temporal predictability on commercial-off-the-shelf (COTS) multiprocessor systems to exploit their high performance for real-time applications. Several solutions proposed in the real-time literature have hardware requirements that are not easily satisfied by modern COTS platforms, like hardware support for strict memory partitioning or the presence of scratchpads. However, even without said hardware support, it is possible to design an efficient memory-centric scheduler. In this article, we design, implement, and analyze a memory-centric scheduler for deterministic memory management on COTS multiprocessor platforms without any hardware support. Our approach uses fixed-priority scheduling and proposes a global "memory preemption" scheme to boost real-time schedulability. The proposed scheduling protocol is implemented in the Jailhouse hypervisor and Erika real-time kernel. Measurements of the scheduler overhead demonstrate the applicability of the proposed approach, and schedulability experiments show a 20% gain in terms of schedulability when compared to contention-based and static fair-share approaches

Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representations

Autonomous driving involves complex decision-making in highly interactive environments, requiring thoughtful negotiation with other traffic participants. While reinforcement learning provides a way to learn such interaction behavior, efficient learning critically depends on scalable state representations. Contrary to imitation learning methods, high-dimensional state representations still constitute a major bottleneck for deep reinforcement learning methods in autonomous driving. In this paper, we study the challenges of constructing bird's-eye-view representations for autonomous driving and propose a recurrent learning architecture for long-horizon driving. Our PPO-based approach, called RecurrDriveNet, is demonstrated on a simulated autonomous driving task in CARLA, where it outperforms traditional frame-stacking methods while only requiring one million experiences for efficient training. RecurrDriveNet causes less than one infraction per driven kilometer by interacting safely with other road users.Comment: IEEE International Conference on Intelligent Transportation Systems 202