Revolutionizing road safety: How advanced driver monitoring systems and predictive analytics are reducing risks on road

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Advanced driver monitoring systems and predictive analytics are revolutionising road safety. Our Co-founder and CTO, Neil Unadkat, writes for The Times Of India, shedding light on the groundbreaking technologies that are reducing risks on the road. Read about how cutting-edge technology seamlessly integrates with vehicles, enabling you to constantly monitor driver behaviour while receiving real-time alerts on a range of safety violations. Road safety is undergoing a revolution. Join the movement towards a safer tomorrow.

The Ministry of Road Transport and Highways’ annual report for 2020 revealed that driver negligence was responsible for a staggering 78.3% of the 4,39,123 road accidents in India that year. Such negligence can manifest in various forms, including overspeeding, harsh acceleration, hard braking, unscheduled driving, freerunning, idling, driving under the influence of drugs or alcohol, and disregarding traffic laws and signals. To curb the risks posed by driver negligence, advanced driver monitoring and road safety systems are incorporating modern technology, including AI and predictive analytics. 

The first step in monitoring driver behavior is to collect data points through a variety of sensors. These sensors integrated into a monitoring system help analyze rash and aggressive driving. Advanced systems can even detect alcohol inebriation, significantly reducing the likelihood of accidents due to drunk driving. In addition to this, systems equipped with sensors that detect eye movements, head position, and other biometric data can identify signs of drowsiness and fatigue, further enhancing road safety.  

Once the data has been collected, it is analyzed using predictive analytics to identify patterns and trends that could indicate potential safety risks. Advanced machine learning algorithms are then used to develop a predictive model that can analyze the dataset in real-time and predict potential safety risks. The predictive models can also take into account external factors, such as weather and road conditions, to provide accurate risk assessments. Using this technology, fleet managers can take proactive measures such as providing targeted training, adjusting routes or schedules, and implementing automatic emergency braking systems.

Fleet managers can now leverage driver scorecards to gain a more comprehensive understanding of driver performance and take a proactive approach to mitigate risks. These systems identify areas of concern or risk for drivers, such as frequent accidents or violations, which can be addressed through targeted training sessions, resulting in more effective and efficient instruction tailored to meet the specific needs of each driver. These incentivised models can be employed to promote good driving behaviour, ultimately providing better economic stability to drivers. Apart from improving driver performance, these systems can help establish a safety culture within the organisation, where drivers take responsibility for their safety and that of others on the road.  

The significance of monitoring driver behaviour in the mobility industry cannot be emphasised enough. The sheer number of accidents caused by driver negligence and fatigue is a matter of grave concern, and it is crucial to take immediate action to address this issue. Thanks to advanced technology such as AI and predictive analytics, we have the tools to monitor driver behaviour, identify potential safety risks, and take proactive measures to reduce them. All stakeholders in the mobility industry must work together to ensure the safety of drivers and passengers. By investing in driver behaviour monitoring systems and promoting a culture of safety, we can create safer roads for everyone. It is time to act now and make road safety a top priority.

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