Development of an Automatic Calculation Method for Infrastructure Support Sensor Placement to Eliminate Blind Spots at Intersections

Takahiro Sakai･Teppei Saitoh (Hitachi)

We have developed a method to automatically calculate sensor placement that eliminates blind spots at intersections based on operation design domain (ODD) conditions and road networks in order to reduce the design effort for an autonomous driving system (ADS) that cooperates with infrastructure support sensors in the ODD. As a result, we were confirmed that the method can calculate infrastructure sensor placement that eliminates blind spots from the viewpoint of ADS while considering the degradation of recognition performance due to weather conditions.

Long Baseline Stereo Camera for Adaptive Driving Beam Evaluation

Shunya Kumano･Nao Ikeda･Yumi Yamada･Yusuke Ueda･Naoki Kawasaki (SOKEN)･Kiichiro Kawakami (Toyota Motor)

The auto high beam is a function to detect other vehicles and switch the beam so as not to cause glare, and it is expected to operate further to 1 km due to resent improvements in lamp brightness. As a system for automating the performance evaluation, we propose a 90 cm baseline stereo camera and a measurement technology that satisfies the ranging accuracy of -20/+10% by disparity calculation robust to light source flare.

Proposal of a Steering Assist System Considering Individual Driver's Operational Input Constraints

Daisuke Nagasaka (J-QuAD DYNAMICS)･Akira Ito･Hiroyuki Okuda (Nagoya University)･Sigenori Ichinose･Yosuke Omori･Katsumasa Koike･Yusuke Fujii (J-QuAD DYNAMICS)

This presentation proposes a novel vehicle lateral motion control method that adapts to the individual steering range, diverging from conventional control methods based on the average driver. By considering each driver's operable steering range, it dynamically controls yaw movement and reactionary torque. This approach allows even drivers with physical constraints to achieve smooth steering, aiming to provide a comfortable and safe driving experience without the need for complex steering operations.

Autonomous Emergency Braking Performance in Electric Vehicles and Influence on the Japan New Car Assessment Program (JNCAP)

Álvaro Esquer Molina･Marc Llaó (Applus+ IDIADA)

In the context of this research, we explore AEB technology with a focus on improving its performance in terms of efficiency and driver acceptance. Our investigation involves studying the current AEB system's performance and comparing it with the braking capabilities of the vehicle. Furthermore, we explore the integration of Advanced Driver Assistance Systems (ADAS) in electric vehicles (EVs). Within this context, we scrutinize their applications within the framework of the Japan New Car Assessment Program (JNCAP), with the objective of augmenting overall safety outcomes.