Existing lane-changing (LC) algorithms in general prioritize self-interest above the benefits of others. We argue that an autonomous vehicle should be socially responsible during lane change without causing excessive impact on its surroundings. Thus, in this paper, we propose an LC algorithm that could generate a trajectory to optimize the overall benefits of the subject vehicle and others in its vicinity. This is mainly achieved by introducing Longitudinal Control Model to characterize the driving behaviors of neighboring vehicles, and drivers’ needs such as comfort, efficiency, and safety are simultaneously satisfied. We combine macro and micro comparative analysis to evaluate the advantage of our approach against existing algorithms. Our findings reveal that the vehicle controlled by our algorithm is capable of safely performing the LC maneuver while causing the least amount of impact. This is reflected in the decrease in total cost of all vehicles and the increase in traffic speed and throughput in its vicinity. In addition, we use HighD dataset for further validation and demonstrate that our algorithm is practically sound. Application of this research can lead to improved autonomous driving technology.

As one of the key algorithms in supporting AV (autonomous vehicle) to complete the LC (lane changing) maneuver, the LTP (LC trajectory planning) algorithm generates safe and efficient LC trajectory for the AV. This paper proposes a novel dynamic LTP algorithm based on the quintic polynomial curve. This algorithm is capable of adjusting LC trajectory according to the state changes of the surrounding driving environment. The formulation of our proposed algorithm mainly consists the underlying form of trajectory equation, the optimization objective function, the corresponding constrains, and the SQP (sequential quadratic programming) algorithm. For each planning step, the time-based quintic polynomial function is introduced to model the trajectory equation. The problem of solving the parameters of the corresponding equation is then transformed into an optimization problem, which takes driver’s safety, comfort, and efficiency into account. After that, the SQP algorithm is employed to solve this optimization problem. Finally, both numerical simulation and field-data validation are used to verify the effectiveness of our proposed algorithm. We anticipate that the research could provide certain valuable insights for developing more reliable LC algorithms for AVs.

Incorporation of Human Factors (HF) into the mathematical car-following (CF) models has always been the research hotspot. Ignorance of such inclusion would inevitably hinder us from acquiring a comprehensive understanding of traffic flow phenomena. This paper proposed a novel CF model in order to bridge three existing research gaps: the demand for more inclusion of HF into the Longitudinal Control Model (LCM); the requirement for a more desirable underlying CF model for the Task Capability Interface (TCI) model; the ignorance of the fuzziness of human brains when modeling Task Difficulty (TD). Specifically, in order to imitate driver’s natural or subjective uncertainty and ambiguity of his TD, the fuzzy logic approach is introduced, and the TD is then incorporated into the LCM. Thereafter, both numerical simulation and field-data validation have been performed. Results indicate that our proposed model is more capable of accommodating HF and exhibits better performance than its predecessor.

Understanding the spatiotemporal characteristics of merging behavior is crucial for the advancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merging patterns, and investigate how various factors, such as merging scenarios and vehicle types, influence driving behavior. Initially, a framework based on a high-definition (HD) map is developed to extract trajectory information in a meticulous manner. Subsequently, eight distinct merging patterns are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane’s spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifies high-risk situations. Utilizing the latest aerial dataset, exiD, which provides HD map data, the study presents novel findings. Specifically, it uncovers patterns where the following vehicle in the target lane chooses to accelerate and overtake rather than cutting in front of the merging vehicle, resulting in Time-to-Collision (TTC) values of less than 2.5 s, indicating a significantly higher risk. Moreover, the study finds that differences in merging speed, distance, and duration can be disregarded in patterns where vehicles are present both ahead and behind, or solely ahead, suggesting these patterns could be integrated for simulation to streamline analysis and model development. Additionally, the practice of truck platooning has a significant impact on vehicle merging behavior. Overall, this study enhances the understanding of merging behavior, facilitating autonomous vehicles’ ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic management, and enabling the effective integration of autonomous driving systems with human drivers.

Understanding the spatiotemporal characteristics of merging behavior is crucial for the ad-vancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merg-ing patterns, and investigate how various factors, such as merging scenarios and vehicle types, in-fluence driving behavior. Initially, a framework based on High-Definition (HD) Map is devel-oped to extract trajectory information in a meticulous manner. Subsequently, eight distinct merg-ing patterns (Pattern A to H) are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane's spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifying high-risk situations. Our novel findings reveal that Patterns C and F, with Time-to-Collision (TTC) values less than 2.5 seconds, pose a significantly higher risk than other patterns. In traffic simulations, Patterns B and E can be integrated, as they show negligible differences in speed, distance traveled, and duration. Additionally, the practice of truck platooning has a signif-icant impact on vehicle merging behavior. This study enhances the understanding of merging be-havior, facilitating autonomous vehicles' ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic manage-ment, and enabling the effective integration of autonomous driving systems with human drivers.