# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
## 3 Results
### Future of AV Deployment in Rural Areas
#### 3.3.3 Necessary Infrastructure Modifications  
While significant resources have advanced onboard AV technologies such as sensors, control systems, and remote-operation capabilities, less focus has been placed on modernizing roadway infrastructure to support these innovations. This disparity has resulted in increasingly complex onboard systems designed to handle inconsistent road conditions and unpredictable behaviors of humans and animals. For seamless AV integration, aligning technological advancements with strategic infrastructure improvements is crucial. [PERSON] (2019)[PERSON] (2019)[PERSON] (2019)[PERSON] (2019) proposed a layered infrastructure approach, classifying roads as \"self-driving eligible\", \"depot-to-depot\", and \"last-mile\" routes, each requiring tailored modifications. V2I systems and advanced traffic management are critical for real-time data sharing and safe operation in mixed traffic. Safety measures must address interactions with pedestrians, animals, and agricultural vehicles to ensure effective AV deployment in rural areas. The field-test-based studies emphasize the need for robust physical and digital infrastructure to support AV deployment. Advanced ADS-equipped vehicles collected extensive data to identify infrastructure deficiencies, such as poor lane visibility, faded markings, and inconsistent lane designs, which often led to sensor misdetections ([PERSON], 2017; [PERSON], 2018). Physical enhancements include upgraded road surfaces with clear, high-contrast markings, retroreflective and wet-reflective pavement markings for improved visibility under adverse conditions, and connectivity-ready roads ([PERSON], 2018). The Drive MN study stressed updating pavement marking guidelines, addressing outdated or confusing markings, implementing dotted line markings for better lane guidance, and incorporating performance standards into traffic engineering manuals ([PERSON], 2018). The goMARTI project highlighted optimal stop locations near parking lot entrances or trail crossings and explored rural-adapted mobility hubs for improved accessibility ([PERSON], 2018). The CASSI program also recommended temporary modular ramps at stops to accommodate riders with mobility devices ([PERSON], 2018). Robust digital infrastructure, including cloud-based data processing, HD mapping, and reliable communication systems, is essential for seamless AV operations ([PERSON], 2018; [PERSON], 2018). In rural areas, frequent HD map updates are necessary to reflect unpredictable road conditions such as unpaved roads, steep gradients, and sharp curves. CASSI pilots at Cary's Bond Park and UNC Charlotte tested V2I systems with RSU-equipped traffic signals for better integration, highlighting infrastructure readiness as critical for success ([PERSON], 2018; [PERSON], 2018). Modifications, such as adding charging stations, localized GPS with RTK base stations, and accessibility features like modular ramps, were identified as key for successful CAV integration. [PERSON] ([PERSON], 2018) stressed upgrading traffic management systems to address rural traffic patterns, including roundabouts, stop signs, and unregulated crossings. Sensors, cameras, and detection systems at intersections are considered essential for safe AV navigation and accommodating road users like agricultural vehicles, freight trucks, and wildlife. [PERSON] et al. (2022)[PERSON] (2018) also emphasized refining high-speed maneuvers to improve user comfort and support broader ADS acceptance among transportation-challenged populations. Focusing on a simulation-based approach,Abohassan et al. (2024) [41] investigated AV interactions with static rural environments using a LiDAR-based simulation framework. Digital replicas of rural roads in Alberta, Canada, were created via the VISTA simulator to assess how roadside vegetation, road geometry, and weather conditions affect AV data processing needs. Findings revealed that roadside elements could increase data demands by up to 400%, while heavy rain raised requirements by 240%.