A CAD-Based Approach to Wrong-Way Driving Analysis and Prevention Using Iot and AI

Authors: Sonali Subhash Nagariya, Sanket Eknath Mohite, Snehal Navnath Navale, Payal Satish Mandlik, Chaitali M Gunjal

DOI: https://doi.org/10.37082/IJIRMPS.v14.i1.232948

Short DOI: https://doi.org/hbp5jx

Country: India

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Abstract: Wrong-way driving (WWD) is a serious problem for road safety that often results in fatal collisions, especially on national roads, intersections, and exit ramps. Poor signage visibility, careless driving, a lack of real-time preventive systems, and a lack of knowledge about road layouts are the main causes of these incidents in India. This study examines wrong-way driving behaviour and provides a cost-effective, state-of-the-art solution that makes use of the Internet of Things (IoT) and artificial intelligence (AI) for quicker detection and reaction.
In order to identify potential wrong-way entry points, traffic flow patterns, and accident-prone areas, a thorough field assessment was carried out along National Highway NH-52 as part of the study. Additionally, formal consultations with traffic officials were conducted at the Regional Transport Offices (RTOs) of Beed and Srirampur. These discussions yielded valuable information about the common causes of incidents involving wrong-way driving, the difficulties in enforcing the law, and the practical limitations of the current monitoring systems.
The suggested system design was greatly influenced by these real-world observations.
Based on the survey and institutional input, a sensor-based wrong-way driving detection system is recommended; sensors are the primary detection mechanism due to their affordability, reliability, and ability to function well in a range of environmental conditions. Cameras are only used as a secondary verification and evidence-support tool to reduce reliance on expensive vision-based systems. AI-based reasoning is used to confirm vehicle heading and minimize false detections, while IoT enables real-time alarm production to traffic control authorities for faster response.
The system employs a multi-phase safety strategy, starting with audio-visual alerts for drivers and progressing to non-destructive rumble strips to warn distracted drivers. An inventive controlled air-release spike mechanism is suggested as a last line of defence. Unlike conventional spike strips that cause an abrupt tire puncture, the proposed design allows for slow tire deflation through micro perforations. This reduces the possibility of further incidents and guarantees controlled deceleration. Direction-specific and speed-sensitive activation enhances operational safety even more.
Because the recommended approach places a high priority on human safety, cost-effective implementation, and legal viability, it can be selectively implemented at important highway locations, such as designated black spots and exit ramps. The study concludes that while addressing real-world problems observed in field surveys and RTO contacts, a sensor-first, safety-focused approach in conjunction with AI and IoT can significantly improve the avoidance of wrong-way driving.

Keywords: Keywords: Driving Against Traffic, CAD-Enhanced Road Design, Internet of Things, AI, Roadway Safety, Smart Transportation Networks.

Paper Id: 232948

Published On: 2026-02-20

Published In: Volume 14, Issue 1, January-February 2026

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