Conflict Point Detection Using AI for Safer IRC:65 Intersection Planning

Intersection safety is a critical focus area within India’s rapidly expanding road network. As urbanisation increases and traffic volumes grow, junctions experience complex vehicle and pedestrian interactions that are difficult to assess using traditional methods. Effective road asset management India now depends on advanced digital tools that can interpret real-world traffic behaviour. Within this context, AI-based conflict point detection is emerging as a powerful approach to support safer IRC:65 intersection design and strengthen data-driven planning.

IRC:65 provides detailed geometric and operational guidance for at-grade intersections. However, design compliance alone does not guarantee safety. This gap between design intent and on-ground behaviour is where AI intersection safety analysis and intelligent traffic safety systems deliver measurable value.

Why Intersection Safety Under IRC:65 Needs Operational Intelligence?

IRC:65 aims to minimise conflict points through channelisation, controlled movements, adequate sight distance and proper pedestrian facilities. In reality, Indian intersections operate under mixed traffic conditions, variable compliance and high behavioural unpredictability.

While an intersection may meet geometric requirements, unsafe manoeuvres such as late lane changes, aggressive turning and informal pedestrian crossings still occur. Addressing these risks requires moving beyond drawings and into operational performance assessment using AI-based junction safety assessment methods.

‍

Understanding Conflict Points in IRC:65 Intersection Design

Conflict points represent locations where vehicle or pedestrian paths cross, merge or diverge. IRC:65 focuses on reducing the number and severity of these interactions through layout optimisation.

However, not all conflict points carry equal risk. Some interactions occur repeatedly without incidents, while others result in near-misses that precede serious crashes. AI-based conflict point detection identifies and measures these unsafe interactions objectively, allowing engineers to prioritise interventions based on actual risk rather than assumptions.

‍

Why Traditional Safety Audits Are Not Enough?

Conventional intersection safety reviews rely on short-term field observations and historical crash data. This approach has inherent limitations in India.

1. Near-miss events are never recorded
2. Crash data is often delayed or incomplete
3. Observations depend on individual judgement
4. Mixed traffic behaviour is difficult to quantify

As a result, risks remain hidden until serious incidents occur. Integrating AI-based road safety audit techniques enables continuous, unbiased safety evaluation.

‍

How AI-Based Conflict Point Detection Works?

Using video data captured from fixed cameras or mobile survey vehicles, AI systems track the movement of every road user. Each vehicle, pedestrian or cyclist is converted into a digital trajectory.

When trajectories come dangerously close in time and space, the system identifies a conflict. Parameters such as approach speed, angle and time-to-collision are analysed to assess severity. This process forms the foundation of AI intersection safety analysis and supports evidence-based safety decisions aligned with IRC:65 principles.

‍

Supporting IRC:65 Compliance Through AI Analysis

AI insights help engineers verify whether channelisation islands, turning lanes and pedestrian crossings are functioning as intended. By comparing detected conflict patterns with IRC:65 design objectives, planners can identify design deficiencies that are not apparent on drawings.

This strengthens planning decisions for both new intersections and improvement of existing junctions, ensuring safer outcomes without deviating from IRC standards.

‍

AI-Based Junction Safety Assessment in Mixed Traffic Conditions

Indian intersections accommodate diverse road users with different speed profiles and movement patterns. AI-based junction safety assessment excels in such environments by analysing all users simultaneously.

This allows identification of risky behaviours such as two-wheelers weaving through turning traffic or pedestrians crossing outside designated zones. These insights directly support proactive safety improvements under modern intelligent traffic safety systems.

‍

Integration With Road Asset Management Systems

Safety performance is closely linked to the condition and visibility of road assets. Conflict analysis becomes more effective when integrated into broader asset workflows.

For example, linking AI safety outputs with road inventory inspection highlights whether missing signs or faded markings contribute to unsafe movements. Pavement friction and surface condition insights from digital pavement condition survey help explain braking-related conflicts near intersections.

‍

Role of Traffic Exposure in Conflict Evaluation

Conflict frequency alone does not define risk. Exposure plays a key role. Integrating movement data from traffic survey allows engineers to normalise conflicts against traffic volumes.

This ensures that high-risk intersections are prioritised accurately, supporting smarter investment and safer network planning.

‍

How RoadVision AI Enables Smarter Intersection Planning?

RoadVision AI enables scalable deployment of AI-driven intersection safety solutions aligned with Indian road standards. The platform combines conflict detection, safety auditing and asset data into a unified workflow.

Real-world applications are showcased through case studies, while technical insights and best practices are shared on the RoadVision AI blog. These implementations demonstrate how AI strengthens IRC:65 compliance while improving safety outcomes.

‍

Conclusion

While IRC:65 establishes strong design guidance, true intersection safety depends on real-world performance. AI-based conflict point detection bridges this gap by revealing behavioural risks that traditional methods miss. By integrating automated intersection safety analysis and AI-based road safety audit into planning workflows, authorities can enhance safety, optimise design and strengthen road asset management in India in a measurable and sustainable way.

RoadVision AI is revolutionizing roads and transforming infrastructure development and maintenance with its innovative solutions in AI in roads. By leveraging Artificial Intelligence and advanced computer vision, the platform conducts thorough road safety audits, ensuring the early detection of potholes and other surface issues for timely repairs and improved road conditions. The integration of potholes detection and data-driven insights through AI also enhances traffic surveys, addressing congestion and optimizing road usage. Focused on creating smarter roads, RoadVision AI ensures compliance with  IRC Codes, empowering engineers and stakeholders to reduce costs, minimize risks, and elevate road safety and transportation efficiency.

Book a demo with us

‍

FAQs

‍

Q1. How does AI support IRC:65 intersection planning?

AI evaluates real traffic behaviour to validate design effectiveness beyond drawings.

‍

Q2. Can AI be applied to existing intersections?

Yes AI analyses video data without requiring physical modifications.

‍

Q3. Does AI replace traditional safety audits?

No AI enhances audits by providing objective and continuous safety data.

‍