The ecosystem of connected devices and sensors (the Internet of Things) has moved a step ahead with the introduction of Cognitive IoT. Cognition is associated with the human brain and refers to thinking as a synthesis of understanding, learning, and reasoning. Artificial Cognition is the calculated integration of computational data results generated by a machine programmed to simulate human thought processes. Cognitive IoT is then the process of implementing cognitive computing technologies that lets IoT devices react to changes in the environment without human intervention.
System sensors are enabled to grasp structured and unstructured data to make meaning of it by establishing patterns and formats. The reasoning of the system refers to its ability to opt for the right model or pattern to solve an arising issue without prior solutions to questions on it. Learning implies the quality of automatic inference and understanding of the new data along with existing data. Together, they bring into action Cognitive IoT that enables reliable decision-making as impeccable Business Intelligence (BI) across enterprise organizations. More recently, Cognitive IoT as situation awareness and intelligence based on sensing, computation, and communication has made it an integral part of vehicle safety.
Drivers, passengers, pedestrians, infrastructure, all stand to gain as vehicles embrace Cognitive IoT. The system receives data, organizes, classifies, and restructures it continually as part of its learning process following required parameters. In the process, it strengthens human safety through analysis of humans and other activities that enable it to establish and predict real-time data with accuracy.
Managing Risks with Connected Vehicular Technology
Connected vehicular technology lets vehicles communicate with internal and external environments using a range of technologies, predominantly wireless technologies. The vehicles use wireless networks to create interactions between onboard sensors and those that are outside. It establishes Vehicle to Vehicle (V2V) or Vehicle to Infrastructure (V2I) communication to facilitate smooth and accident-free movement of vehicles and ensures the safety of those in and around.
Connected vehicles are well-positioned to prevent accidents with their enhanced collision warning technology. Sensors are also able to recognize pedestrians on the street or dangerous intersections. Connected vehicles can talk to each other to inform, and act on data received in real-time, facilitating safe travel. Raw IoT data from other vehicles is processed and analyzed in a centralized analytics system that provides safe, and actionable traffic management. Vehicles can avoid inconvenience and interruptions with easy route detours based on real-time information from the system.
V2V communication is the ultimate car safety value proposition that Cognitive IoT has to offer. Nearby connected vehicles alert each other on adverse road conditions, speed, and more. The technology is capable of initiating preventive measures such as automatic braking or giving out apt driver alerts to avoid possible collisions.
V2I connects vehicles with physical surroundings where the system helps prevent accidents by setting out driver alerts on road conditions, and weather ahead, helping navigate smooth, incident-free journeys.
Driver Feedback and Predictive Maintenance
Cognitive IoT is here to issue spoken alerts to drivers in case of over-speeding. Sensors monitor surroundings and respond by giving alerts to drivers in all such scenarios. Since the sensors are connected to smartphones, they prevent drivers from engaging in making calls or sending out texts to avoid distractions and possible accidents.
Intelligently acting sensors predict vehicle conditions when they detect a potential problem. It is part of predictive maintenance cognition that lets vehicle owners avoid dangerous on-road conditions or sudden breakdowns. Onboard telematics system predicts the impeding defect reducing chances of malfunction and risk to driver, and vehicle.
Cognitive Internet of Vehicles Architecture
Cognitive Internet of Vehicles (CIoV) consists of layers of AI architecture:
- Sensing and participation: Sensors, Cameras, Lidar, Radar, and the like, collect raw data from the external environment. The data input is processed by the system software to decide on the course of action such as lane changing, acceleration, and overtaking.
- Network communication and data acquisition: This is a network-based communication among different transportation entities aiming at transport-related data acquisition. Intra-vehicular communication involves sensors in-vehicle monitoring. They detect a range of conditions such as road conditions, driver fatigue, tire pressure, and other autonomous control sensors. The sensors communicate with each other and take intelligent decisions for human drivers in emerging situations like a crash warning, adaptive cruise control, and self-parking.
- Edge computing and data pre-processing: In this computational process, data from all sources is stored and processed. Vehicular cloud provides and also participates in real-time services like navigation, traffic monitoring, crash warning, parking availability, and more.
- Cognition and control: Cloud-based dynamic cognition and utilization of computing resources using Machine Learning, Deep Learning, Neural Networking carry out cognitive data processing.
- Application: Coordination and collaboration with different automatic and mobile connected services fulfill the objective of driver assistance, and better traffic management.
Use Case: How Honda cars use Cognitive IoT
Honda leverages a ready-to-use IoT platform for its connected car movements. The software can capture data based on the distance to other vehicles, distance to other objects, vehicle placement on the lane, break distance and timing, and driver behavior. The system uses all of this information to carry out real-time analysis letting vehicles adapt and adjust to any driving behavior. It protects drivers by issuing early warnings of dangerous driving situations.
Summing it up
A broad range of vehicle sensors collect data about the surrounding environment, process them in an intelligent environment of computational algorithms that understand, reason, and initiate the best possible action in a situation. AI-powered vision and signal processing techniques gather information around the road environment, interpret, and model the information to make necessary decisions. The entire sequence simulates human cognition and action – giving it the name Cognitive IoT.
While traveling on road, the technology acts as a self-controlled robot that operates independently and takes a wide range of decisions by interpreting traffic scenarios. Connected vehicles communicate with each other sharing information on vision, driving intents, pedestrians, and other vehicles in proximity help prevent collisions or any other hazardous condition. Yes, the era of smart vehicles is already here, and at Ascentt, we are proud to be helping automobile giants in realizing their smart cars initiatives.