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.

Wide band antenna for mobile communications

Abstract: An antenna to be mounted on a vehicle. The antenna includes a ground plate with an L-shaped radiator plate and an L-shaped sub-radiator plate. One leg of the radiator plate and sub-radiator plate are parallel to the ground plate. The parallel legs are spaced from the ground plate. The other leg is perpendicular to the ground plate. The perpendicular leg is separated from the ground plate by a small gap. The two legs are opposed to each other with a gap between the legs parallel to the ground plate. In some embodiments, filler is placed between the plates.

Power supply system and vehicle with the system

Abstract: A vehicle includes a charging unit receiving electric power from an external power source and externally charging a power storage unit. When a connector unit is coupled to the vehicle and a state ready for charging by the external power source is attained, a controller predicts degradation ratio of the power storage unit at the time point of completion of external charging based on degradation characteristic of the power storage unit in connection with SOC and battery temperature obtained in advance, and sets target state of charge of each power storage unit based on the battery temperatures so that the predicted degradation ratio does not exceed tolerable degradation ratio at the time point of completion of external charging. Then, the controller controls corresponding converters such that SOCs of power storage units attain the target states of charge.

Single-Photon Avalanche Diode with Enhanced NIR-Sensitivity for Automotive LIDAR Systems.

Abstract: A single-photon avalanche diode (SPAD) with enhanced near-infrared (NIR) sensitivity has been developed, based on 0.18 μm CMOS technology, for use in future automotive light detection and ranging (LIDAR) systems. The newly proposed SPAD operating in Geiger mode achieves a high NIR photon detection efficiency (PDE) without compromising the fill factor (FF) and a low breakdown voltage of approximately 20.5 V. These properties are obtained by employing two custom layers that are designed to provide a full-depletion layer with a high electric field profile. Experimental evaluation of the proposed SPAD reveals an FF of 33.1% and a PDE of 19.4% at 870 nm, which is the laser wavelength of our LIDAR system. The dark count rate (DCR) measurements shows that DCR levels of the proposed SPAD have a small effect on the ranging performance, even if the worst DCR (12.7 kcps) SPAD among the test samples is used. Furthermore, with an eye toward vehicle installations, the DCR is measured over a wide temperature range of 25–132 °C. The ranging experiment demonstrates that target distances are successfully measured in the distance range of 50–180 cm.

Fast algorithms for large scale conditional 3D prediction

Abstract: The potential success of discriminative learning approaches to 3D reconstruction relies on the ability to efficiently train predictive algorithms using sufficiently many examples that are representative of the typical configurations encountered in the application domain. Recent research indicates that sparse conditional Bayesian mixture of experts (cMoE) models (e.g. BME (Sminchisescu et al., 2005)) are adequate modeling tools that not only provide contextual 3D predictions for problems like human pose reconstruction, but can also represent multiple interpretations that result from depth ambiguities or occlusion. However, training conditional predictors requires sophisticated double-loop algorithms that scale unfavorably with the input dimension and the training set size, thus limiting their usage to 10,000 examples of less, so far. In this paper we present large-scale algorithms, referred to as fBME, that combine forward feature selection and bound optimization in order to train probabilistic, BME models, with one order of magnitude more data (100,000 examples and up) and more than one order of magnitude faster. We present several large scale experiments, including monocular evaluation on the HumanEva dataset (Sigal and Black, 2006), demonstrating how the proposed methods overcome the scaling limitations of existing ones.