Artificial Intelligence and Deep Learning Technology Race to Determine Winners and Losers in Quest for Driverless Cars
London, United Kingdom - 09 Nov 2015
Artificial Intelligence (AI), and in particular Deep Learning based on neural network computing, parallel processing and unassisted cloud-based crowd learning are driving key innovations in automotive. AI technology application areas include machine vision and speech recognition, both of which have huge relevance for automotive and transportation:
- Virtual Assistants – Advanced agents knowing the driver’s preferences and allowing natural language interaction within the vehicle and driving context. Apple’s 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. Nissan’s Intelligent Driving System (IDS) concept includes a virtual assistant.
- Vehicle Automation – Advanced Driver Assistance Systems (ADAS) and driverless vehicles will heavily rely on 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.
Read ABI Research’s Artificial Intelligence, Deep Learning and Collective Intelligence in Automotive Transportation report.
AI is dominating the automotive headlines with recent announcements from NVIDIA (Tegra X1 and Deep Learning), Panasonic (pedestrian recognition demoed at ITS World Congress), Mitsubishi Electric (cognitive driver distraction detection), Nissan (IDS concept), Toyota (partnerships with MIT and Stanford), Amazon (AWS IoT platform and Amazon Machine Learning), Tesla (Autopilot), Siemens (radar-based parking space detection), IBM (Watson, TrueNorth SyNAPSE chipset, prognostics), Baidu (Baidu Brain autopilot, Baidu Institute of Deep Learning and Duer assistant), Neurosoft and many more.
“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,” says Dominique Bonte, Vice President and General Manager at ABI Research.
However, the relevance of AI goes far beyond individual vehicles. Deep Learning intrinsically is a collective learning experience, harnessing and harvesting the crowd intelligence of millions of vehicles to accelerate the machine learning cycles. Moreover, it also involves including intelligent roadside infrastructure and the data it generates from traffic cameras, road sensors and toll gates. This will ultimately lead to far reaching convergence between connected driverless vehicles and ITS, resulting in holistic, remotely controlled and automatically reconfiguring closed loop transportation systems with traffic throughput optimization heavily relying on demand-response approaches.
These findings are part of ABI Research’s Automotive Safety and Autonomous Driving and Smart Transportation Research Services, which includes research reports, market data, insights and competitive assessments.
About ABI Research
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- Artificial Intelligence, Deep Learning, and Collective Intelligence in Automotive and Transportation
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