Description

This issue of Automotive and Transportation TOE highlights key developments, applications, and advancements in ADAS sensing, processing, and software technologies.

The purpose of the Automotive and Transportation (A&T) Technology Opportunity Engine (TOE) is to raise awareness of global technology innovations in self-propelled ground-based mobile platforms that are not only technically significant, but potentially offering commercial value. Each monthly A&T TOE provides subscribers valuable descriptions and analyses of 8 noteworthy innovations, written by a qualified TechVision automotive engineer affiliated with SAE International (the Society of Automotive Engineers). The main focus is on highway-licensed motor vehicles (light, medium and heavy). Passenger cars, trucks, buses, motorcycles, scooters and railway locomotives are within the product scope, energized by any fuel. Many of the innovations concern powertrains (internal combustion engines, turbines, battery electrics, fuel cell electrics, hybrid-electrics), as well as drivetrains (including transmissions), interiors--seating and displays, advanced materials--as for body/chassis, wireless connectivity, and self-driving technology that is currently receiving so much attention. The A&T TOE outlines and evaluates each innovation, notes which organizations and developers are involved, projects the likely timing for commercialization, furnishes a patent analysis, and provides valuable strategic insights for industry stakeholders.

The need for low power, smaller, lighter sensors with enhanced performance attributes and minimal false alarms is driving innovations in the sensors space. The Sensors and Control cluster covers innovations pertaining to technologies such as wireless sensors and networks, energy harvesting, haptics and touch, MEMS and nanosensors, Terahertz, ubiquitous/smart sensors, CBRNE, quantified-self, sensor fusion, M2M communications, and drones.

Keywords: advanced driver assistance system, automated vehicles, Lidar, sensor fusion, pedestrian detection, neural networks, system-on-chip