While the global automotive and transportation industry seems laser-focused on exploring new upside opportunities associated with the Internet of Things (IoT), another cluster of technology research and development is about to emerge into the mainstream marketplace.
Artificial Intelligence (AI) -- in particular, Deep Learning based on neural network computing -- parallel processing and unassisted cloud-based crowd learning are propelling new innovations within the established consumer vehicle and commercial transport sectors.
According to the latest worldwide market study by ABI Research, two key technologies and associated use-cases are noteworthy. AI technology application areas include machine vision and speech recognition, both of which have huge relevance for the automotive and transportation sectors.
Exploring AI Use Case Scenarios
Virtual Assistants – Advanced agents knowing the driver's preferences and allowing natural language interaction within the vehicle and driving context. Apple Siri, Google Now, and Nuance Dragon represent early examples of in-vehicle integration and adaptation of virtual assistants. Microsoft announced intentions to develop an automotive-grade version of Cortana. The Nissan Intelligent Driving System (IDS) concept includes a virtual assistant.
Vehicle Automation – Advanced Driver Assistance Systems (ADAS) and driverless vehicles will heavily rely upon deep learning-based machine vision for identifying and recognizing pedestrians and vehicle types, as well as interpreting and predicting complex traffic situations.
Traffic Management Automation – Adaptive Traffic Lights, dynamic pricing for Electronic Toll Collection (ETC) and Road User Charging (RUC), and future holistic automated Intelligent Transport Systems (ITS) will be powered by advanced artificial intelligence, far exceeding the capabilities of human operators at traffic operation centers today.
Deep Learning within a Transportation Borg
AI is just beginning to populate the automotive industry news headlines, with recent announcements from numerous multinational vendors -- including Panasonic, Mitsubishi Electric and Siemens.
"AI is the latest hype in automotive, with an arms race taking place among car OEMs, Tier1 suppliers, Internet and IT players and silicon vendors to develop, control or acquire the deep learning technology which will drive disruptive change though both automation and advanced user interfaces and HMI. Apple recently poaching NVIDIA's deep learning expert is just one example of the AI war heating up," said Dominique Bonte, vice president at ABI Research.
ABI believes that the relevance of AI technology goes far beyond individual vehicles. Deep Learning intrinsically is a collective borg-like learning experience -- harnessing and harvesting the crowd intelligence of millions of vehicles to accelerate the machine learning cycles. The future potential applications are vast in scope.
Moreover, these complex systems also include intelligent roadside infrastructure and the data it generates from traffic cameras, road sensors and toll gates. This data assimilation will ultimately lead to far reaching convergence between connected driverless vehicles and ITS, resulting in holistic, remotely controlled and automatically reconfiguring closed-loop transportation networks with traffic throughput optimization heavily relying on intelligent demand-response approaches.
Artificial Intelligence (AI) -- in particular, Deep Learning based on neural network computing -- parallel processing and unassisted cloud-based crowd learning are propelling new innovations within the established consumer vehicle and commercial transport sectors.
According to the latest worldwide market study by ABI Research, two key technologies and associated use-cases are noteworthy. AI technology application areas include machine vision and speech recognition, both of which have huge relevance for the automotive and transportation sectors.
Exploring AI Use Case Scenarios
Virtual Assistants – Advanced agents knowing the driver's preferences and allowing natural language interaction within the vehicle and driving context. Apple Siri, Google Now, and Nuance Dragon represent early examples of in-vehicle integration and adaptation of virtual assistants. Microsoft announced intentions to develop an automotive-grade version of Cortana. The Nissan Intelligent Driving System (IDS) concept includes a virtual assistant.
Vehicle Automation – Advanced Driver Assistance Systems (ADAS) and driverless vehicles will heavily rely upon deep learning-based machine vision for identifying and recognizing pedestrians and vehicle types, as well as interpreting and predicting complex traffic situations.
Traffic Management Automation – Adaptive Traffic Lights, dynamic pricing for Electronic Toll Collection (ETC) and Road User Charging (RUC), and future holistic automated Intelligent Transport Systems (ITS) will be powered by advanced artificial intelligence, far exceeding the capabilities of human operators at traffic operation centers today.
Deep Learning within a Transportation Borg
AI is just beginning to populate the automotive industry news headlines, with recent announcements from numerous multinational vendors -- including Panasonic, Mitsubishi Electric and Siemens.
"AI is the latest hype in automotive, with an arms race taking place among car OEMs, Tier1 suppliers, Internet and IT players and silicon vendors to develop, control or acquire the deep learning technology which will drive disruptive change though both automation and advanced user interfaces and HMI. Apple recently poaching NVIDIA's deep learning expert is just one example of the AI war heating up," said Dominique Bonte, vice president at ABI Research.
ABI believes that the relevance of AI technology goes far beyond individual vehicles. Deep Learning intrinsically is a collective borg-like learning experience -- harnessing and harvesting the crowd intelligence of millions of vehicles to accelerate the machine learning cycles. The future potential applications are vast in scope.
Moreover, these complex systems also include intelligent roadside infrastructure and the data it generates from traffic cameras, road sensors and toll gates. This data assimilation will ultimately lead to far reaching convergence between connected driverless vehicles and ITS, resulting in holistic, remotely controlled and automatically reconfiguring closed-loop transportation networks with traffic throughput optimization heavily relying on intelligent demand-response approaches.