Dynamic Model-Based Safety Margins for High-Density Matrix Headlight Systems

Real-time masking of vehicles in a dynamic road environment is a demanding task for adaptive driving beam systems of modern headlights. Next-generation high-density matrix headlights enable precise, high-resolution projections, while advanced driver assistance systems enable detection and tracking of objects with high update rates and low-latency estimation of the pose of the ego-vehicle. Accurate motion tracking and precise coverage of the masked vehicles are necessary to avoid glare while maintaining a high light throughput for good visibility. Safety margins are added around the mask to mitigate glare and flicker caused by the update rate and latency of the system. We provide a model to estimate the effects of spatial and temporal sampling on the safety margins for high- and low-density headlight resolutions and different update rates. The vertical motion of the ego-vehicle is simulated based on a dynamic model of a vehicle suspension system to model the impact of the motion-to-photon latency on the mask. Using our model, we evaluate the light throughput of an actual matrix headlight for the relevant corner cases of dynamic masking scenarios depending on pixel density, update rate, and system latency. We apply the masks provided by our model to a high beam light distribution to calculate the loss of luminous flux and compare the results to a light throughput approximation technique from the literature

Deep Learning Based Classification of Pedestrian Vulnerability Trained on Synthetic Datasets

The reliable detection of vulnerable road users and the assessment of the actual vulnerability is an important task for the collision warning algorithms of driver assistance systems. Current systems make assumptions about the road geometry which can lead to misclassification. We propose a deep learning-based approach to reliably detect pedestrians and classify their vulnerability based on the traffic area they are walking in. Since there are no pre-labeled datasets available for this task, we developed a method to train a network first on custom synthetic data and then use the network to augment a customer-provided training dataset for a neural network working on real world images. The evaluation shows that our network is able to accurately classify the vulnerability of pedestrians in complex real world scenarios without making assumptions on road geometry

Sub-Microsecond Time Synchronization for Network-Connected Microcontrollers

This paper presents a bare-metal implementation of the IEEE 1588 Precision Time Protocol (PTP) for network-connected microcontroller edge devices, enabling sub-microsecond time synchronization in automotive networks and multimedia applications. The implementation leverages the hardware timestamping capabilities of the microcontroller (MCU) to implement a two-stage Phase-locked loop (PLL) for offset and drift correction of the hardware clock. Using the MCU platform as a PTP master enables the distribution of a sub-microsecond accurate Global Positioning System (GPS) timing signal over a network. The performance of the system is evaluated using master-slave configurations where the platform is synchronized with a GPS, an embedded platform, and a microcontroller master. Results show that MCU platforms can be synchronized to an external GPS reference over a network with a standard deviation of 40.7 nanoseconds, enabling precise time synchronization for bare-metal microcontroller systems in various applications

Synthetic Aperture Radar Algorithms on Transport Triggered Architecture Processors using OpenCL

Live SAR imaging from small UAVs is an emerging field. On-board processing of the radar data requires high-performance and energy-efficient platforms. One candidate for this are Transport Triggered Architecture (TTA) processors. We implement Backprojection and Backprojection Autofocus on a TTA processor specially adapted for this task using OpenCL. The resulting implementation is compared to other platforms in terms of energy efficiency. We find that the TTA is on-par with embedded GPUs and surpasses other OpenCL-based platforms. It is outperformed only by a dedicated FPGA implementation. © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Ethernet-based lighting-architecture : Image stabilization for high-resolution light functions

Single pair Ethernet in combination with Ethernet endpoints provides a scalable basis for the direct control of sensors and actuators in zonal vehicle networks. As recently shown, this approach is also ideal for driving high-resolution light functions. The ability to transmit different parallel data streams to actuators opens a wide field for new applications. Here, we show a method for stabilising high-resolution light projections in driving operation. The stabilization of the light image is based on an inertial measurement unit that records vehicle movements in real-time. An algorithm in a central control unit continuously calculates correction values for the position and distortion compensation of the light distribution and sends this data to the lamp via Ethernet, preferably 10BASE-T1S. Two methods are combined in a proof of concept: predictive correction with video data rate and image shifting in the headlamp’s frame buffer at high frequency

Genomic and transcriptomic resources for marker development in synchytrium endobioticum, an elusive but severe potato pathogen

Synchytrium endobioticum is an obligate biotrophic fungus that causes wart diseases in potato. Like other species of the class Chytridiomycetes, it does not form mycelia and its zoospores are small, approximately 3 μm in diameter, which complicates the detection of early stages of infection. Furthermore, potato wart disease is difficult to control because belowground organs are infected and resting spores of the fungus are extremely durable. Thus, S. endobioticum is classified as a quarantine organism. More than 40 S. endobioticum pathotypes have been reported, of which pathotypes 1(D1), 2(G1), 6(O1), 8(F1), and 18(T1) are the most important in Germany. No molecular methods for the differentiation of pathotypes are available to date. In this work, we sequenced both genomic DNA and cDNA of the German pathotype 18(T1) from infected potato tissue and generated 5,422 expressed sequence tags (EST) and 423 genomic contigs. Comparative sequencing of 33 genes, single-stranded confirmation polymorphism (SSCP) analysis with polymerase chain reaction fragments of 27 additional genes, as well as the analysis of 41 simple sequence repeat (SSR) loci revealed extremely low levels of variation among five German pathotypes. From these markers, one sequence-characterized amplified region marker and five SSR markers revealed polymorphisms among the German pathotypes and an extended set of 11 additional European isolates. Pathotypes 8(F1) and 18(T1) displayed discrete polymorphisms which allow their differentiation from other pathotypes. Overall, using the information of the six markers, the 16 isolates could be differentiated into three distinct genotype groups. In addition to the presented markers, the new collection of EST from genus Synchytrium might serve in the future for molecular taxonomic studies as well as for analyses of the host-pathogen interactions in this difficult pathosystem. © 2017 The American Phytopathological Society.Federal Ministry of Food and Agricultur