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.

Traffic Signal Control Based on Reinforcement Learning with Graph Convolutional Neural Nets

Abstract: Traffic signal control can mitigate traffic congestion and reduce travel time. A model-free reinforcement learning (RL) approach is a powerful framework for learning a responsive traffic control policy for short-term traffic demand changes without prior environmental knowledge. Previous RL approaches could handle high-dimensional feature space using a standard neural network, e.g., a convolutional neural network; however, to control traffic on a road network with multiple intersections, the geometric features between roads had to be created manually. Rather than using manually crafted geometric features, we developed an RL-based traffic signal control method that employs a graph convolutional neural network (GCNN). GCNNs can automatically extract features considering the traffic features between distant roads by stacking multiple neural network layers. We numerically evaluated the proposed method in a six-intersection environment. The results demonstrate that the proposed method can find comparable policies twice as fast as the conventional RL method with a neural network and can adapt to more extensive traffic demand changes.

Semantically-Guided Representation Learning for Self-Supervised Monocular Depth

Abstract: Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns. In this work we investigate how to leverage more directly this semantic structure to guide geometric representation learning, while remaining in the self-supervised regime. Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions. Furthermore, we propose a two-stage training process to overcome a common semantic bias on dynamic objects via resampling. Our method improves upon the state of the art for self-supervised monocular depth prediction over all pixels, fine-grained details, and per semantic categories.

Object detection device

Abstract: An object detection device, including: an imaging unit (400) that is mounted on a movable body; an object detection unit (201) that calculates an image displacement of a partial image between two images captured by the imaging unit (400) at different times, and performs detection processing to detect an object in an image based on at least the image displacement; and a control unit (201) that changes a manner of performing the detection processing based on a position in the image in a lateral direction of the movable body.

Predictive destination entry for a navigation system

Abstract: A predictive destination entry system for a vehicle navigation system to aid in obtaining a destination for the vehicle. The navigation system utilizes a memory for storing data relating to prior driving history or habits. A processor connected with the memory examines the information stored in the memory for making predictions for the current destination desired by a user of the vehicle. The information stored in the memory may be segregated into distinct user profiles and may include the vehicle location, previous driving history of the vehicle, previous searching history of a user of the vehicle, or sensory input relating to one or more characteristics of the vehicle. The navigation system may either confirm the predicted destination with the user of the vehicle before generating or displaying a travel route to the predicted destination or may automatically generate and display the travel route without verifying the destination with the user.