The Road Safety Problem
The global statistics on road traffic crashes are unsettling. According to the World Health Organization (WHO), road traffic crashes are responsible for 1.25 million deaths and between 20 and 50 million non-fatal injuries every year[i]. If left unchecked, road traffic accidents are forecasted to become the seventh-leading cause of death by 2030[ii]. Among the leading causes of fatal road accidents are alcohol-impairment, drowsiness and distracted driving, which were responsible for 10,497, 3,450 and 803 fatalities respectively in the United States in 2016.[iii] Using machine learning technologies, Affectiva, an emotion artificial intelligence company, seeks to enhance driver safety in collaboration with car manufacturers to address these primary causes of road accidents.
Machine Learning at Affectiva
Founded in 2009 in Cambridge, Massachusetts, Affectiva is a developer of emotion-sensing artificial intelligence software through the analysis of human facial expressions and physiological responses. Its products have been used in a variety of applications including market research, advertisement assessment, customer service robots, augmented reality system for autism. The company is now actively exploring Automotive Artificial intelligence to monitor drivers and enhance road safety.
Machine learning is at the very core of Affectiva’s business model and is the basis of its product development. The company has developed an emotion data repository through the analysis of over 6 million faces from 87 countries. This data is used to train its deep learning (a branch of machine learning) algorithms to detect various emotion metrics such as contempt, anger, fear, joy, sadness and surprise.[iv]
Given the breadth of potential applications within the emotion artificial intelligence space, Affectiva can continue to leverage its emotion data repository as it expands and explores various opportunities. The continuous improvement of its algorithms and data repository will be key to its long-term success.
Automotive Artificial Intelligence – How will it work?
Using cameras installed in a car, Affectiva Automotive AI will monitor a driver’s facial expressions in real time to determine a driver’s cognitive and emotional state, leveraging a subset of its emotion data repository for automotive use cases. In addition, the system will detect and analyze voice activity in the vehicle[v]. The data collected will be provided to auto manufacturers, enabling them to build advanced monitoring systems to take appropriate action, if unsafe conditions are detected. Automakers have begun installing cameras inside some new car models that track the movement of the driver’s head and eyes. The system, which is already being used in at least one General Motors car, will become standard equipment on many European cars in a couple of years.[vi]
Exhibit 1. Source: Affectiva
Affectiva’s management has established strategic partnerships with other AI industry leaders such as Nuance Communications Inc, a developer of speech-recognition systems used in an estimated 200 million cars worldwide. This partnership enabled the voice detection capabilities for the Automotive AI product. The integration with Nuance will begin to establish Affectiva in the automotive industry and deliver the industry’s first interactive automotive assistant that understands drivers’ and passengers’ complex cognitive and emotional states from face and voice and adapts behavior accordingly[vii]. Long-term, the company is carefully considering the ethical implications of Automotive AI by avoiding non-consensual data and evaluating ways to avoid introducing bias into their algorithms[viii], which is a primary concern in this space.
Based on the amount of data Affectiva has gathered in its emotion data repository for its automotive use cases (See Exhibit 1), I would recommend that the company targets its Automotive AI product development to the United States market in the short term. Given Affectiva’s lack of experience in the automotive industry, the company should focus on working with select manufacturers in the US to extensively test its technology, gather data and continue to refine its algorithms and establish itself in the US before attempting to expand into other markets.
[i] World Health Organization. (2018). Road Traffic Injuries, http://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
[ii] World Health Organization. (2018). Road Traffic Injuries, http://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
[iii] National Highway Traffic Safety Administration (2017) US DOT Releases Fatal Traffic Crash Data. https://www.nhtsa.gov/press-releases/usdot-releases-2016-fatal-traffic-crash-data
[iv] Affectiva (2018), http://go.affectiva.com/auto
[vi] Bray, Hiawatha. (2018, September) Back seat driver? New car cams may soon sense driver fatigue, texting, other distractions. https://www.bostonglobe.com/business/2018/09/27/back-seat-driver-new-car-cams-may-soon-sense-driver-fatigue-texting-other-distractions/z7qpS02xcjA49xMvgqeyhP/story.html
[vii] Affectiva and Nuance to Bring Emotional Intelligence to AI-Powered Automotive Assistants (2018) https://www.marketwatch.com/press-release/affectiva-and-nuance-to-bring-emotional-intelligence-to-ai-powered-automotive-assistants-2018-09-06
[vii] Press, Gil (2017, June) Emerging Artificial Intelligence (AI) Leaders: Rana el Kaliouby, Affectiva. https://www.forbes.com/sites/gilpress/2017/06/12/emerging-artificial-intelligence-ai-leaders-rana-el-kaliouby-affectiva/#67cb0d613d46