Mitsubishi has developed a driving detector technology that senses when a driver is distracted or absent-minded. Once implemented in the future, this could improve the overall safety of drivers by quickly alerting them with a reminder to pull over or take a break.
Although there are current systems that detect when a driver is drowsy, cognitive distractions are far more difficult to sense because its symptoms are less apparent. This industry-first technology has a machine-learning algorithm. The algorithm analyzes time series data that compares vehicle information and driver information, such as heart rate or facial orientation. Once a threat is sensed, the driving detector will initiate an alert regarding the perceived dangerous driving. This technology will be displayed at the Tokyo Motor Show in 2015, and its installation in cars is not predicted until 2019.
Distracted Driving Detectors
Future Mitsubishi Cars Will Be Able to Detect Absent-Minded Drivers
Trend Themes
1. Cognitive Distraction Detection - Developing a machine learning algorithm that can compare vehicle and driver information to detect cognitive distractions presents an opportunity for disruptive innovation in the automotive industry.
2. Real-time Alert Systems - Implementing a real-time alert system that can immediately notify the driver of potential hazards or dangers while driving presents an opportunity for disruptive innovation in the automotive industry.
3. Integration of Machine Learning - Integrating machine learning algorithms with automotive technology to create safer driving experiences presents an opportunity for disruptive innovation in the automotive industry.
Industry Implications
1. Automotive - The automotive industry can benefit from developing and implementing cognitive distraction detection technology in cars.
2. Technology - The technology industry can collaborate with the automotive industry to incorporate real-time alert systems in future vehicles.
3. Insurance - The insurance industry can explore partnerships with the automotive industry to encourage the implementation of machine learning algorithms in vehicles to reduce accident rates.