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<p>This study presents an autonomous ground vehicle prototype optimized for India’s complex road conditions, focusing on road curvature and weather impacts. The system integrates RP LIDAR A1M8, Intel RealSense Depth Camera, HC-SR04 ultrasonic sensor, neo6m GPS, and TF LUNA 1D LIDAR, alongside advanced control algorithms. Steering utilizes a path-tracking algorithm based on a kinematic bicycle model, while acceleration is managed via a PID controller. Testing on roads with 0°–50° curvature under sunny and rainy conditions demonstrated high accuracy, reaching 98.2% on straight roads in optimal weather but dropping to 75.3% at 50° curvature. Rain significantly reduced sensor reliability, with LIDAR accuracy decreasing from 98% to as low as 82% in heavy rain and depth camera clarity declining by up to 40% under the same conditions. These results underscore the challenges of autonomous navigation in India, highlighting the need for improved sensor robustness and control strategies to enhance safety and performance across diverse environmental conditions.</p>

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Assessment of path-planning algorithms for autonomous navigation on unstructured Indian roads considering effects of weather and road curvature

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Vivek Patkar,
  • Prachi Bhoir,
  • Ninad Mehendale

摘要

This study presents an autonomous ground vehicle prototype optimized for India’s complex road conditions, focusing on road curvature and weather impacts. The system integrates RP LIDAR A1M8, Intel RealSense Depth Camera, HC-SR04 ultrasonic sensor, neo6m GPS, and TF LUNA 1D LIDAR, alongside advanced control algorithms. Steering utilizes a path-tracking algorithm based on a kinematic bicycle model, while acceleration is managed via a PID controller. Testing on roads with 0°–50° curvature under sunny and rainy conditions demonstrated high accuracy, reaching 98.2% on straight roads in optimal weather but dropping to 75.3% at 50° curvature. Rain significantly reduced sensor reliability, with LIDAR accuracy decreasing from 98% to as low as 82% in heavy rain and depth camera clarity declining by up to 40% under the same conditions. These results underscore the challenges of autonomous navigation in India, highlighting the need for improved sensor robustness and control strategies to enhance safety and performance across diverse environmental conditions.