# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
## 3 Results
### AV Simulation-Based Studies in Rural Areas  
Different road environments require specific behavioral models to accurately simulate traffic. Urban and rural areas demand different models, with most microscopic traffic simulations designed for urban or freeway networks. Commonly used models include AIMSUN, VISSIM, Paramics, MITSIMLab, and CORSIM. In contrast, relatively few models focus on simulating two-lane highways with oncoming traffic. Key models for rural networks include the two-lane passing (TWOPAS) model [86], the traffic on rural roads (TRARR) model [87], and the VTISim model [88], which later evolved into the rural road traffic simulator (RuTSim) [89]. A core element across all environments is the car-following model, while speed adaptation models are needed to calculate desired speeds. For urban and freeway settings, lane-changing decision models are essential, simulating driver lane-change decisions based on traffic flow. For two-lane highways, more complex overtaking models are required to simulate lane changes, passing maneuvers, and returns to the original lane. Gap-acceptance models are crucial in both lane-changing and overtaking scenarios, as well as at intersections, lane drops, and on-ramp merges. Desired speed on urban and freeway roads is typically influenced by speed limits, while rural road speeds depend on factors like road width and curves. Speed adaptation models adjust speed preferences based on these characteristics. On rural highways, where overtaking opportunities are limited, vehicle platoons commonly form. Therefore, rural simulations must focus on platoon formation rather than individual vehicle movement [90]. [PERSON] (2009) [91] focused on enhancing realism in driving simulators for rural areas by developing models that reflected vehicle platooning and overtaking behavior on two-lane highways. The study refined a microscopic traffic simulation model (VTISim) and introduced a moving window approach to simulate nearby vehicles in detail while optimizing computational efficiency. An algorithm managed transitions between autonomous and controlled vehicle behaviors, balancing realism and reproducibility. The simulations emphasized the importance of modeling vehicle platoons, common on rural highways due to limited overtaking opportunities. Experiments confirmed realistic traffic flows, with overtaking behavior and speed adaptation accurately simulated, although some vehicles displayed aggressive tendencies typical of rural roads. [PERSON] et al. (2024) [6] developed a framework for safe, personalized autonomous overtaking on two-lane rural roads. It integrated perception, navigation, and control layers. The perception layer modeled the environment using real-time sensor data, while the navigation layer employed a finite state machine and a fuzzy inference system to adapt overtaking styles. Relaxed settings prioritized comfort,while sporty settings enabled dynamic maneuvers. The control layer managed braking, acceleration, and steering to ensure collision-free, efficient trajectories. Simulations with Carmaker demonstrated effective performance, with dynamic safety checks optimizing user comfort and road safety. Simulation-based studies on AVs in rural areas (Table 2) focus on reducing delays, enhancing curve navigation, ensuring safe overtaking, and optimizing environmental and operational benefits for cost-effective rural transport.