Use of Artificial Intelligence in Road Safety

Syllabus: GS2/Governance

Context

• The Union Minister of Road Transport & Highways, addressed the 12th edition of the Traffic InfraTech Expo.
  • He emphasized the critical need to improve road safety and the adoption of advanced technologies in the transportation sector.

About

• India experiences around 5 lakh accidents each year, resulting in numerous fatalities.
  • More than half of these casualties are in the age group of 18-36 years. 
  • The economic loss due to road accidents is estimated at 3% of the country’s GDP. 
• The government has decided to appoint experts from the private sector to collaborate on developing technological solutions.
  • It will evaluate proposals from startups and industry leaders, ensuring that the best ideas are implemented. 
  • The committee has been directed to finalize its evaluations within three months.

Use of Technology in Transport Sector

• Traffic Management: AI systems analyze real-time traffic data to optimize signal timings, reduce congestion, and improve overall traffic flow.
  • This can lead to fewer accidents caused by gridlock or unpredictable traffic patterns.
• Predictive Analytics: By analyzing historical accident data, AI can identify high-risk areas and times, allowing authorities to implement targeted safety measures.
• Driver Assistance Systems: AI is integrated into vehicle systems to provide features like lane departure warnings, collision avoidance, and adaptive cruise control. 
• Emergency Response: AI systems can optimize routes for emergency vehicles, ensuring quicker response times during accidents, which can be crucial for saving lives.
• Identifying traffic violations through AI can allow authorities to enforce penalties accurately. 
• Upgrading toll collection methods, including the exploration of satellite toll systems would improve efficiency and ensure transparency in toll collection.

Challenges

• Infrastructure Limitations: Many areas lack the necessary infrastructure, such as reliable internet connectivity and adequate sensor networks, to support AI technologies effectively.
• Data Privacy Concerns: The collection and analysis of large volumes of traffic and personal data raise concerns about privacy and data security.
• Quality of Data: The quality of traffic and accident data in India varies significantly, making it difficult to develop reliable AI systems.
• Integration with Existing Systems: Integrating AI solutions with existing traffic management systems and regulatory frameworks can be complex and require significant investment.
• Skill Gaps: There is a shortage of skilled professionals in AI and data analytics within India.
  • This limits the ability to develop, implement, and maintain AI systems effectively.
• Ethical Considerations: The deployment of AI in critical areas like traffic management raises ethical questions, such as bias in algorithms and accountability in case of failures or accidents.

Way Ahead

• Data Standardization: Establish standard protocols for data collection and sharing among various stakeholders.
• Public-Private Partnerships: Encourage collaboration between government agencies, private companies, and academic institutions to leverage resources, expertise, and technology for developing AI solutions.
• Skill Development Programs: Implement training programs to build a workforce skilled in AI, data analytics, and machine learning.
• Pilot Projects: Launch pilot projects in select cities to test AI applications in real-world scenarios.
• Ethical Guidelines: Establish ethical guidelines for AI development and deployment, focusing on transparency, accountability, and bias mitigation to ensure fair treatment and public trust.
• Feedback Mechanisms: Create channels for public feedback on AI systems and road safety initiatives, allowing for continuous improvement based on user experiences and concerns.

Source: PIB