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Research is under way to achieve autonomous driving (AD) on urban roads by 2020. This study focused on a right-turn situation at an intersection difficult to navigate even for human drivers. Many accidents have been reported when turning right due to poor visibility of traffic in an oncoming lane. An AD system is being developed that can recognize and avoid a potential risk of accident where another vehicle at an intersection causes poor visibility. The aim is to automate identification of vehicle locations and locations with poor visibility as well as decisions as to whether to decelerate, accelerate, or stop. As solutions, an obstacle detection frame (ODF) was created to identify the locations of other vehicles and a visibility detection frame (VDF) was created to identify the locations of blind spots. Detection frames are defined in an internal intersection map in the AD system.

Flat panel displays for automobiles are facing a new era with the development of navigation systems. As navigation systems become more important as driver's assistance devices, development of birds-eye-view and 3D displays continues, as well as improvements for larger display screens and higher mounting positions. In response to the progress of mobile multimedia technologies, demands for larger display screens and larger aspect ratios have been increasing. Significance for improvements to anti-glare features or view angles has increased as they provide better visibility and the increase layout options. The use of human machine information interaction, which interfaces visual, audio and tactile senses, makes it possible to realize safer, more convenient and comfortable multimedia era vehicle

A new map display technique has been developed for car navigation systems that presents a bird's-eye view of three-dimensional map images. This view allows both detailed and wide-area information to be presented simultaneously in a single screen display. It provides drivers with a reassuring feeling while driving along their route because the two types of information can guide them accurately to their destination. The results of evaluation tests have confirmed the effectiveness of this technique. On the other hand, some aspects requiring further improvement were also identified. This paper discusses how the technique was developed and describes the tests conducted to evaluate its effectiveness along with the results obtained.

This paper describes an Expert System which can diagnose Nissan's Electronic Concentrated engine Control System (ECCS). This system employs the production system as its AI technique. The knowledge of expert mechanics on cause and effect relations is expressed in “problem - cause” pairs. The development of this prototype system for engine troubleshooting has confirmed that it is possible to systematize and store the knowledge of experts in a computer. Now by, adding more sophisticated expert knowledge, this system can be made an effecvtive tool in providing quick and accurate diagnosis.