Weakly Supervised Semantic Segmentation for Range-Doppler Maps

Deep convolutional neural networks (DCNNs) have been successfully applied for object detection and semantic segmentation of radar range-Doppler (RD) maps. However, training a DCNN requires many annotated examples that are costly and difficult to create. In this work we present a method that reduces significantly the manual effort involved in the annotation of RD maps to train a DCNN for segmentation. A 40 times reduction in manual labelling effort is achieved because the annotation of each RD map includes only the class of the objects instead of drawing a polygon around the corresponding cells. The localization of the objects is performed by tracing back from the output to the input of a classification neural network. Experimental results show that our approach achieves robust localization performance in complex real-world urban scenarios as observed with a low-cost automotive radar. Furthermore, we show that our approach performs similarly to DCNNs that are trained with a publicly available dataset in which localization information is provided.</p

Computation of Buffer Capacities for Throughput Constrained and Data Dependent Inter-Task Communication

Streaming applications are often implemented as task graphs. Currently, techniques exist to derive buffer capacities that guarantee satisfaction of a throughput constraint for task graphs in which the inter-task communication is data-independent, i.e. the amount of data produced and consumed is independent of the data values in the processed stream. This paper presents a technique to compute buffer capacities that satisfy a throughput constraint for task graphs with data dependent inter-task communication, given that the task graph is a chain. We demonstrate the applicability of the approach by computing buffer capacities for an MP3 playback application, of which the MP3 decoder has a variable consumption rate. We are not aware of alternative approaches to compute buffer capacities that guarantee satisfaction of the throughput constraint for this application

Association of Camera and Radar Detections Using Neural Networks

Automotive radar and camera fusion relies on linear point transformations from one sensor's coordinate system to the other. However, these transformations cannot handle non-linear dynamics and are susceptible to sensor noise. Furthermore, they operate on a point-to-point basis, so it is impossible to capture all the characteristics of an object. This paper introduces a method that performs detection-to-detection association by projecting heterogeneous object features from the two sensors into a common high-dimensional space. We associate 2D bounding boxes and radar detections based on the Euclidean distance between their projections. Our method utilizes deep neural networks to transform feature vectors instead of single points. Therefore, we can leverage real-world data to learn non-linear dynamics and utilize several features to provide a better description for each object. We evaluate our association method against a traditional rule-based method, showing that it improves the accuracy of the association algorithm and it is more robust in complex scenarios with multiple objects.</p

Buffer Capacity Computation for Throughput Constrained Streaming Applications with Data-Dependent Inter-Task Communication

Streaming applications are often implemented as task graphs, in which data is communicated from task to task over buffers. Currently, techniques exist to compute buffer capacities that guarantee satisfaction of the throughput constraint if the amount of data produced and consumed by the tasks is known at design-time. However, applications such as audio and video decoders have tasks that produce and consume an amount of data that depends on the decoded stream. This paper introduces a dataflow model that allows for data-dependent communication, together with an algorithm that computes buffer capacities that guarantee satisfaction of a throughput constraint. The applicability of this algorithm is demonstrated by computing buffer capacities for an H.263 video decoder

An Abstraction-Refinement Theory for the Analysis and Design of Real-Time Systems

Component-based and model-based reasonings are key concepts to address the increasing complexity of real-time systems. Bounding abstraction theories allow to create efficiently analyzable models that can be used to give temporal or functional guarantees on non-deterministic and non-monotone implementations. Likewise, bounding refinement theories allow to create implementations that adhere to temporal or functional properties of specification models. For systems in which jitter plays a major role, both best-case and worst-case bounding models are needed. In this paper we present a bounding abstraction-refinement theory for real-time systems. Compared to the state-of-the-art TETB refinement theory, our theory is less restrictive with respect to the automatic lifting of properties from component to graph level and does not only support temporal worst-case refinement, but evenhandedly temporal and functional, best-case and worst-case abstraction and refinement