This paper presents an approach for the robust recognition of a complex and dynamic driving
environment, such as an urban area, using on-vehicle multilayer LIDAR. The multilayer …

Driving vehicles requires mastery of a multitude of tasks. Among these, parking is one task
that most drivers feel they are not as skilled as they would like to be. In this paper, we focus on …

This study specifically examines wall see-through visualization for drivers at blind corners to
prevent crossing collisions. We believe that realizing the desired effect with the simplest …

Auto-encoders constitute one popular deep learning architecture for feature extraction. Since
an auto-encoder has at least one bottle neck layer for feature representation and at least …

In terms of computational neuroscience, several theoretical learning schemes have been
proposed to acquire suitable motor controllers in the human brain. The controllers have been …

Recently, preventive safety systems such as Forward Collision Warning (FCW)(1) and
Autonomous Emergency Braking (AEB)(2) have been made commercially available. Their …

A forward‐propagation learning scheme has been proposed to acquire inverse models of
controlled objects in multilayered neural networks. This scheme is quite different from back‐…

It has been suggested that inverse models serve feedforward controllers in the human brain.
We have proposed a novel learning scheme to acquire a neural inverse model of a …

In environments where many automated guided vehicles (AGVs) operate, planning efficient,
collision-free paths is essential. Related research has mainly focused on environments with …

A forward‐propagation learning rule (FPL) has been proposed for a neural network (NN) to
learn an inverse model of a controlled object. A feature of FPL is that the trajectory error …