Toyota

About: Toyota is a company organization based out in Safenwil, Switzerland. It is known for research contribution in the topics: Internal combustion engine & Battery (electricity). The organization has 40032 authors who have published 55003 publications receiving 735317 citations. The organization is also known as: Toyota Motor Corporation & Toyota Jidosha KK.

Microfailure behaviour of randomly dispersed short fibre reinforced thermoplastic composites obtained by direct sem observation

Abstract: The microfailure behaviour of thermoplastic polyamide 6,6 composites reinforced with randomly dispersed short glass fibres was studied. Scanning electron microscopy was carried out on the surface of the composites under load to observe directly the behaviour. The microfailure proceeds following the steps (1) interfacial microfailure occurs at the fibre tips, (2) the microfailure propagates along the fibre sides, (3) plastic deformation bands of the matrix occurs from the interfacial one, (4) crack opening occurs in the band and the crack grows slowly through the band, (5) finally a catastrophic crack propagation occurs through the matrix with pulling-out fibres from the matrix. A model for the microfailure mechanism of the composites is proposed and some methods to improve the mechanical properties of the composites are discussed on the basis of the mechanism.

Self-Supervised Model Adaptation for Multimodal Semantic Segmentation

Abstract: Learning to reliably perceive and understand the scene is an integral enabler for robots to operate in the real-world. This problem is inherently challenging due to the multitude of object types as well as appearance changes caused by varying illumination and weather conditions. Leveraging complementary modalities can enable learning of semantically richer representations that are resilient to such perturbations. Despite the tremendous progress in recent years, most multimodal convolutional neural network approaches directly concatenate feature maps from individual modality streams rendering the model incapable of focusing only on the relevant complementary information for fusion. To address this limitation, we propose a mutimodal semantic segmentation framework that dynamically adapts the fusion of modality-specific features while being sensitive to the object category, spatial location and scene context in a self-supervised manner. Specifically, we propose an architecture consisting of two modality-specific encoder streams that fuse intermediate encoder representations into a single decoder using our proposed self-supervised model adaptation fusion mechanism which optimally combines complementary features. As intermediate representations are not aligned across modalities, we introduce an attention scheme for better correlation. In addition, we propose a computationally efficient unimodal segmentation architecture termed AdapNet++ that incorporates a new encoder with multiscale residual units and an efficient atrous spatial pyramid pooling that has a larger effective receptive field with more than $$10\,\times $$ fewer parameters, complemented with a strong decoder with a multi-resolution supervision scheme that recovers high-resolution details. Comprehensive empirical evaluations on Cityscapes, Synthia, SUN RGB-D, ScanNet and Freiburg Forest benchmarks demonstrate that both our unimodal and multimodal architectures achieve state-of-the-art performance while simultaneously being efficient in terms of parameters and inference time as well as demonstrating substantial robustness in adverse perceptual conditions.

SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation

Abstract: Recent techniques in self-supervised monocular depth estimation are approaching the performance of supervised methods, but operate in low resolution only. We show that high resolution is key towards high-fidelity self-supervised monocular depth prediction. Inspired by recent deep learning methods for Single-Image Super-Resolution, we propose a subpixel convolutional layer extension for depth super-resolution that accurately synthesizes high-resolution disparities from their corresponding low-resolution convolutional features. In addition, we introduce a differentiable flip-augmentation layer that accurately fuses predictions from the image and its horizontally flipped version, reducing the effect of left and right shadow regions generated in the disparity map due to occlusions. Both contributions provide significant performance gains over the state-of-the-art in self-supervised depth and pose estimation on the public KITTI benchmark. A video of our approach can be found at https://youtu.be/jKNgBeBMx0I.

SiC—Emerging Power Device Technology for Next-Generation Electrically Powered Environmentally Friendly Vehicles

Abstract: The automotive industry is developing a range of electrically powered environmentally friendly vehicles such as hybrid vehicles (HVs), plug-in hybrid vehicles, full electric vehicles, and fuel cell vehicles to help reduce tailpipe CO2 emissions and achieve energy diversification. HVs are regarded as one of the most practical types of environmentally friendly vehicle and have already been widely accepted in the market. Toyota Motor Corporation has positioned HV systems as a core technology that can be applied to all next-generation electrically powered environmentally friendly vehicles and is currently working to enhance the performance of HV system components. Because of its low loss and high-temperature operation properties, silicon carbide (SiC) is regarded as a highly promising material for power semiconductor devices to help reduce the size and weight of the power control unit, one of the key components of a HV system. Wide-ranging activities are under way to meet the challenges of adopting SiC in an automotive environment, such as the development of crystal growth technologies, device structures, process technologies, defect analysis, and application to on-board systems. This paper describes the current situation and future prospects for on-board SiC power devices and the development of SiC-based technologies.