Abstract
The present article introduces a MATLAB-based simulation framework for the implementation and assessment of a lane-change system for autonomous vehicles. The framework comprises algorithms for lane detection, decision-making about lane change execution, and display of the vehicle's actions. A synthetic dataset is created to replicate lane-marked roadways, offering a controlled setting for system testing and validation. The framework is additionally assessed utilizing performance criteria including accuracy, precision, and area under the curve (AUC). The validation includes parameter estimation, goodness-of-fit metrics, and graphical comparisons. The results indicate that the Rayleigh model outperforms the other models in capturing the characteristics of lane change behavior.Article Preview
TopUse Of Radar In Lane-Change Problems
Radar systems play a significant role in detecting and analyzing lane-change behavior in real-world traffic scenarios. These systems emit electromagnetic waves to detect and track vehicles by measuring their reflections from other vehicles on the road. The reflection provides information, including the distance to the target vehicle, its relative speed (via Doppler shift), and its angle relative to the radar. This information is crucial for identifying and monitoring vehicles as they perform lane-change maneuvers.
During lane-change analysis, a vehicle’s trajectory and speed characteristics change. Radar can capture these dynamics by continuously monitoring the lateral movement of the vehicle relative to its lane. See Figure 1.
Figure 1.
Lane-change trajectory using Rayleigh distribution
Changes in relative speed and position are measured over time to determine when and where the lane change occurs. Radar is a core component in advanced driver assistance systems, supporting tasks like:
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Lane-Change Assist: Detecting whether it is safe for a vehicle to change lanes based on surrounding traffic.
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Blind Spot Monitoring: Identifying vehicles in adjacent lanes that may pose a collision risk during lane changes.
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Adaptive Cruise Control: Adjusting the vehicle’s speed during and after a lane change to maintain safe distances.