Modern automotive and avionics embedded systems integrate several functionalities that are subject to complex timing requirements. A typical application in these fields is composed of sensing, computation, and actuation. The ever increasing complexity of heterogeneous sensors implies the adoption of multi-rate task models scheduled onto parallel platforms. Aspects like freshness of data or first reaction to an event are crucial for the performance of the system. The Directed Acyclic Graph (DAG) is a suitable model to express the complexity and the parallelism of these tasks. However, deriving age and reaction timing bounds is not trivial when DAG tasks have multiple rates. In this paper, a method is proposed to convert a multi-rate DAG task-set with timing constraints into a single-rate DAG that optimizes schedulability, age and reaction latency, by inserting suitable synchronization constructs. An experimental evaluation is presented for an autonomous driving benchmark, validating the proposed approach against state-of-the-art solutions.

Object detection is arguably one of the most important and complex tasks to enable the advent of next-generation autonomous systems. Recent advancements in deep learning techniques allowed a significant improvement in detection accuracy and latency of modern neural networks, allowing their adoption in automotive, avionics and industrial embedded systems, where performances are required to meet size, weight and power constraints. Multiple benchmarks and surveys exist to compare stateof-the-art detection networks, profiling important metrics, like precision, latency and power efficiency on Commercial-off-the-Shelf (COTS) embedded platforms. However, we observed a fundamental lack of fairness in the existing comparisons, with a number of implicit assumptions that may significantly bias the metrics of interest. This includes using heterogeneous settings for the input size, training dataset, threshold confidences, and, most importantly, platform-specific optimizations, that are especially important when assessing latency and energy-related values. The lack of uniform comparisons is mainly due to the significant effort required to re-implement network models, whenever openly available, on the specific platforms, to properly configure the available acceleration engines for optimizing performance, and to retrain the model using a homogeneous dataset. This paper aims at filling this gap, providing a comprehensive and fair comparison of the best-in-class Convolution Neural Networks (CNNs) for real-time embedded systems, detailing the effort made to achieve an unbiased characterization on cutting-edge system-on-chips. Multi-dimensional trade-offs are explored for achieving a proper configuration of the available programmable accelerators for neural inference, adopting the best available software libraries. To stimulate the adoption of fair benchmarking assessments, the framework is released to the public in an open source repository.

Commonly used metrics for evaluation of object detection systems (precision, recall, mAP) do not give complete information about their suitability of use in safety critical tasks, like obstacle detection for collision avoidance in Autonomous Vehicles (AV). This work introduces the Risk Ranked Recall (R 3 ) metrics for object detection systems. The R 3 metrics categorize objects within three ranks. Ranks are assigned based on an objective cyber-physical model for the risk of collision. Recall is measured for each rank.

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