Better LIDAR for Driving

Compact system could be better connected to smaller devices ...  like minidrones.

Compact, Non-Mechanical 3D LiDAR System Could Make Autonomous Driving Safer

Optica.org,  February 9, 2023

Researchers at Japan's Kyoto University have developed a palm-sized non-mechanical three-dimensional (3D) LiDAR system that combines the capabilities of conventional beam-scanning LiDAR systems and flash LiDAR to improve the safety of autonomous driving. Comprised of a dually modulated photonic-crystal laser (DM-PCSEL) and a time-of-flight camera, the system can measure the distance and automatically track the motion of poorly reflective objects. Said Kyoto University's Susumu Noda, "The DM-PCSEL integrates non-mechanical, electronically controlled beam scanning with flash illumination used in flash LiDAR to acquire a full 3D image with a single flash of light. This unique source allows us to achieve both flash and scanning illumination without any moving parts or bulky external optical elements, such as lenses and diffractive optical elements."

Full Article   

Machine Learning Giving Smarter Driving Advice

 Interesting how this is guided assistance, based on multiple goals.   Makes me recall using process maps to also drive goals.

Home/News/Using ML to Build Maps That Give Smarter Driving Advice/Full Text

ACM TECHNEWS

Using ML to Build Maps That Give Smarter Driving Advice

By MIT Technology Review. June 29, 2021

Scientists at Qatar's Hamad Bin Khalifa University (HBKU) applied machine learning (ML) to develop QARTA, a new automatic mapping service that can enhance traffic management with greater intelligence. HBKU's Rade Stanojevic and colleagues collaborated with the taxi firm Karwa to collect full global positioning system data on its fleet's comings and goings, so QARTA can advise routes for drivers at Karwa and other operators.

Stanojevic said QARTA's deeper understanding of actual road and traffic conditions in the city of Doha helps drivers shorten trips, translating into 5% to 10% greater fleet-wide efficiency.Stanojevic speculates that ML-based routing advice could factor into holistic views of cities, and help fleets slash carbon emissions by avoiding traffic jams.

From MIT Technology Review