Navigating Mixed Traffic: Lateral and Longitudinal Control for Connected and Autonomous Vehicles with a Human-Centric Approach
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Liu, Yongyang
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Abstract
With the emergence of connected and autonomous vehicles (CAVs), the transportation system is undergoing a revolution driven by advanced vehicle automation and connectivity technologies. CAVs, with enhanced situational awareness and advanced automation capabilities, promise to improve traffic safety, smoothness, and efficiency. However, the coexistence of CAVs with human-driven vehicles (HDVs) in mixed-traffic environments presents significant challenges, particularly in the context of lane changes. These challenges include: (i) disruptions to CAV platoons caused by HDV lane changes, (ii) mental discomfort experienced by HDV drivers and CAV users during HDV lane changes, (iii) uncertainties and compromised safety performance in HDV lane-change behavior in mixed traffic, and (iv) diminished control performance of CAVs and comfort for HDV drivers during CAV lane changes. To address these challenges, this dissertation develops human-centric control strategies for CAVs to improve interactions with HDVs in the context of both HDV and CAV lane changes. The contributions are reflected over several interconnected chapters, each building upon the insights and methods introduced in prior chapters.
First, the dissertation proposes a deep reinforcement learning-based proactive longitudinal control strategy for CAVs to preclude disruptive HDV lane-change behaviors that can induce disturbances, and to preserve the smoothness of traffic flow in the CAV platooning control process. In it, a Transformer-based lane-change traffic condition predictor is constructed to predict whether an HDV will likely perform a disruptive lane change under ambient traffic conditions. If no disruptive lane change is predicted, an extended intelligent driver model is activated for the CAV to perform smooth car-following behavior under cooperative CAV platooning control. If a disruptive lane change is predicted, a rainbow deep Q-Network-based lane-change preclusion model is proposed through which the CAV can alter the lane-change traffic condition to preclude the HDV’s lane change. The proposed control strategy can effectively reduce disruptive lane-change maneuvers by a HDV in the vicinity of the CAV and improve string stability performance, and serves as a building block for proactive control in mixed-traffic environments.
Building on this foundation, the dissertation introduces an innovative human-emulation-based proactive longitudinal control strategy for CAVs, to assist HDV lane changes and counteract their negative effects on CAV platoon smoothness. It constructs a Transformer-based behavior predictor to predict the HDV lane change behavior. If a lane change is anticipated, a lane-change assistance model is developed using the proximal policy optimization, which enables the CAV to emulate the lane-change assistance maneuver of human drivers. If no lane change by the HDV is anticipated, a multi-anticipative car-following model is adopted, through which the CAV executes cooperative platooning control. This strategy emulates the courteous lane-change assistance maneuver of human drivers in cooperative lane changes, where the drivers yield the lane-change vehicle through decelerations. By executing legible motions, CAVs can communicate their intentions in advance, assisting HDV drivers in making safe lane changes and promoting smooth CAV platoon operation.
Third, this dissertation expands the previous two strategies by proposing a proactive human-centric control strategy that enhances CAV control performance and mitigates the mental discomfort of both HDV drivers and CAV users. It operates in a hierarchical reinforcement learning framework and comprises a two-level solution to the intricate lane-change management problem. The upper-level task determines whether to assist or preclude the HDV lane change, and the lower-level task executes the corresponding CAV control actions. For situations without potential lane changes, a proximal policy optimization-based car-following control model is developed for the CAV to perform smooth car-following behavior. An adversarial inverse reinforcement learning-based behavior planner is proposed to regulate the lane-change management and car-following control models. This study emphasizes the potential of incorporating human factors into CAV operations to improve overall traffic performance, while ensuring the mental comfort of both HDV drivers and CAV users.
Fourth, to validate the aforementioned strategies, the dissertation applies driving simulator experiments to investigate HDV drivers’ experiences and safety during lane changes in mixed traffic. It first examines both the perceived and objective complexity experienced by HDV drivers during lane changes and explores their behavior evolution in repeated interactions with CAVs. It emphasizes how HDV drivers may be surprised by the way CAVs behave on roads at their first encounter with CAVs, but then can learn to understand CAV driving behavior after sufficient experience and adapt their lane-change behavior accordingly. Next, the dissertation introduces a comprehensive safety performance framework that combines multiple surrogate safety metrics to analyze the safety performance of HDV lane changes in both HDV-only traffic and mixed-traffic conditions. These investigations highlight the dynamic interactions between CAVs and HDVs in mixed-traffic, evolving nature of HDV behavior, and potential targeted interventions to shape HDV lane-change behavior in mixed-traffic environments.
Last, the dissertation proposes a human-like lane-change control strategy for CAVs to produce human-like lane-change behavior to improve CAV-HDV interactions. Operating within a theory of mind framework, the strategy introduces human-like reasoning into CAV operations. It first develops a human-like lane-change model that combines the enhanced control enabled by robot driving behavior and the human expertise of natural human driving behavior. The model produces human-like lane-change maneuvers to improve CAV control performance and mitigate HDV drivers’ mental discomfort. Further, this strategy proposes a HDV behavior predictor that learns human driving patterns to model human decision-making. The predictor anticipates potential responses of HDVs to the CAV lane change, enabling the CAV to perform informed and adaptive lane-change maneuvers that mitigate disruptions and maintain traffic smoothness. This human-like control strategy aims to integrate human intelligence and cognition with advanced CAV capabilities (in situational awareness and reaction times) to promote the smooth integration of CAVs into society.
In summary, this dissertation enhances the understanding of challenges in CAV-HDV interactions during lane changes in mixed traffic, which can guide CAV manufacturers in designing human-centric and adaptive control systems, while informing transportation planners on strategies to support safe and effective CAV deployment. The proposed human-centric control strategies demonstrate significant potential for improving CAV reliability and safety under diverse real-world conditions. By enabling smoother, more intuitive interactions between CAVs and HDVs, these strategies support a smooth transition to a more automated transportation environment, facilitating the measured and scalable adoption of CAV technologies as they continue to evolve.
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Date
2024-12-09
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Dissertation (PhD)