Interval-based clock synchronization with optimal precision

AbstractWe present description and analysis of a novel optimal precision clock synchronization algorithm (OP), which takes care of both precision and accuracy with respect to external time. It relies upon the generic interval-based algorithm of Schmid and Schossmaier [Real-Time Syst. 12 (2) (1997) 173] and utilizes a convergence function based on the orthogonal accuracy algorithm of Schmid [Chicago J. Theor. Comput. Sci. 3 (2000) 3]. As far as precision is concerned, we show that OP achieves optimal worst case precision, optimal maximum clock adjustment, and optimal rate, as does the algorithm of Fetzer and Cristian [Proceedings 10th Annual IEEE Conference on Computer Assurance, Gaithersburg, MD, 1995]. However, relying upon a perception-based hybrid fault model and a fairly realistic system model, our results are valid for a wide variety of node and link faults and apply to very high-precision applications as well: Impairments due to clock granularity and discrete rate adjustment cannot be ignored here anymore. Our accuracy analysis focuses on the nodes’ local accuracy interval, which provides the atop running application with an on-line bound on the current deviation from external time. We show that this bound could get larger than twice the necessary lower bound (“traditional accuracy”), hence OP is considerably suboptimal in this respect

Static and Dynamic Affordance Learning in Vision-based Direct Perception for Autonomous Driving

The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding. Today, most autonomous vehicles are using the mediated perception approach for path planning and control, which highly rely on high-definition 3D maps and real-time sensors. Recent research efforts aim to substitute the massive HD maps with coarse road attributes. In this thesis, We follow the direct perception-based method to train a deep neural network for affordance learning in autonomous driving. The goal and the main contributions of this thesis are in two folds. Firstly, to develop the affordance learning model based on freely available Google Street View panoramas and Open Street Map road vector attributes. Driving scene understanding can be achieved by learning affordances from the images captured by car-mounted cameras. Such scene understanding by learning affordances may be useful for corroborating base-maps such as HD maps so that the required data storage space is minimized and available for processing in real-time. We compare capability in road attribute identification between human volunteers and the trained model by experimental evaluation. The results indicate that this method could act as a cheaper way for training data collection in autonomous driving. The cross-validation results also indicate the effectiveness of the trained model. Secondly, We propose a scalable and affordable data collection framework named I2MAP (image-to-map annotation proximity algorithm) for autonomous driving systems. We built an automated labeling pipeline with both vehicle dynamics and static road attributes. The data collected and annotated under our framework is suitable for direct perception and end-to-end imitation learning. Our benchmark consists of 40,000 images with more than 40 affordance labels under various day time and weather even with very challenging heavy snow. We train and evaluate a ConvNet based traffic flow prediction model for driver warning and suggestion under low visibility condition

Specification and Implementation of the Universal Time Coordinated Synchronization Unit (UTCSU)

. High accurate external clock synchronization can only be achieved with adequate hardware support. We analyze the requirements yielding a specification and implementation of an ASIC running under the acronym UTCSU. It incorporates adder-based clocks to maintain both local time and accuracy, facilities to timestamp packets containing synchronization data, interfaces to couple GPS receivers, moderate application support, and self-test machinery. These novel clocks are distinguished by their fine grained rate adjustability and their hardware support for linear continuous amortization. Apart from addressing design and engineering issues of this chip, the paper provides a basic programming model as well. Keywords: Real-time systems, external clock synchronization, Universal Time Coordinated (UTC), adder-based clock (ABC), linear continuous amortization, accuracy intervals, Application Specific Integrated Circuit (ASIC), Very high speed integrated circuit Hardware Description Language (VHD..