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N. Chen
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Connected Automated Vehicles (CAVs) have the potential to improve traffic operations when they cooperatively maneuver in merging sections. State-of-the-art approaches in cooperative merging either build on heuristics solutions or prohibit mainline CAVs to change lane on multilane highways. This paper proposes a hierarchical cooperative merging control approach that ensures collision-free and traffic-efficient merging through the interaction of a maneuver planner and an operational trajectory controller. The planner predicts future vehicular trajectories, including acceleration trajectories and time instants when lane changes start, in a long horizon up to 50 seconds with a linear prediction model. It establishes the optimal dynamic vehicle sequence in each lane by minimizing predicted traffic disturbances that can propagate upstream and lead to traffic breakdown. During the process, mainline vehicles may change lane to facilitate the on-ramp merging, albeit with a higher ego cost. The operational controller follows the established instructions from the planner and regulates vehicular trajectories with model predictive control in a shorter horizon of 6 seconds. The performance of the designed hierarchical cooperative merging control approach was compared to a cooperative merging method utilizing widely used first-in-first-out rule to establish merging sequences and the same operational controller to generate vehicular trajectories. Systematic comparison shows that the proposed approach consistently results in less disturbances during merging under 528 different scenarios with different traffic states, initial vehicular states, and desired time gap settings. On average, a decrease of 39.18% in disturbances was observed.
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Connected Automated Vehicles (CAVs) have the potential to improve traffic operations when they cooperatively maneuver in merging sections. State-of-the-art approaches in cooperative merging either build on heuristics solutions or prohibit mainline CAVs to change lane on multilane highways. This paper proposes a hierarchical cooperative merging control approach that ensures collision-free and traffic-efficient merging through the interaction of a maneuver planner and an operational trajectory controller. The planner predicts future vehicular trajectories, including acceleration trajectories and time instants when lane changes start, in a long horizon up to 50 seconds with a linear prediction model. It establishes the optimal dynamic vehicle sequence in each lane by minimizing predicted traffic disturbances that can propagate upstream and lead to traffic breakdown. During the process, mainline vehicles may change lane to facilitate the on-ramp merging, albeit with a higher ego cost. The operational controller follows the established instructions from the planner and regulates vehicular trajectories with model predictive control in a shorter horizon of 6 seconds. The performance of the designed hierarchical cooperative merging control approach was compared to a cooperative merging method utilizing widely used first-in-first-out rule to establish merging sequences and the same operational controller to generate vehicular trajectories. Systematic comparison shows that the proposed approach consistently results in less disturbances during merging under 528 different scenarios with different traffic states, initial vehicular states, and desired time gap settings. On average, a decrease of 39.18% in disturbances was observed.
The aim of the thesis is to design coordination strategies for connected automated vehicles near on-ramps considering controller performance, safe lane changing conditions, maneuver planning, and trajectory control. CAVs have enhanced situation awareness with their onboard detection units and vehicle-to-everything communications. They have the potential to improve traffic operations by manoeuvring together under a common goal and by accepting a small time gap. Existing model predictive control controllers rarely check their controllers’ robustness considering the mismatch between vehicle dynamics and prediction models. The existing cooperative merging strategies constrain that on-ramp CAVs merge into mainline traffic after reaching the final desired inter-vehicle distance and/or (merging) speed. That constraint may make them not be applied to scenarios where the length of the on-ramp lane is short and on-ramp CAVs cannot reach desired states before merging. Few methods investigate optimal merging sequences for two conflicting streams of traffic. Besides, mainline CAVs are rarely allowed to change lane during cooperation. This thesis consecutively tackles the aforementioned four points by presenting four coordination strategies that address the mentioned limitations...
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The aim of the thesis is to design coordination strategies for connected automated vehicles near on-ramps considering controller performance, safe lane changing conditions, maneuver planning, and trajectory control. CAVs have enhanced situation awareness with their onboard detection units and vehicle-to-everything communications. They have the potential to improve traffic operations by manoeuvring together under a common goal and by accepting a small time gap. Existing model predictive control controllers rarely check their controllers’ robustness considering the mismatch between vehicle dynamics and prediction models. The existing cooperative merging strategies constrain that on-ramp CAVs merge into mainline traffic after reaching the final desired inter-vehicle distance and/or (merging) speed. That constraint may make them not be applied to scenarios where the length of the on-ramp lane is short and on-ramp CAVs cannot reach desired states before merging. Few methods investigate optimal merging sequences for two conflicting streams of traffic. Besides, mainline CAVs are rarely allowed to change lane during cooperation. This thesis consecutively tackles the aforementioned four points by presenting four coordination strategies that address the mentioned limitations...
Journal article
(2020)
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N. Chen, B. van Arem, Tom Alkim, M. Wang
Gap selection and dynamic speed profiles of interacting vehicles at on-ramps affect the safety and efficiency of highway merging sections. This paper puts forward a hierarchical control approach for Connected Automated Vehicles (CAVs) to achieve efficient and safe merging operations. A tactical layer controller employs a second-order car-following model with a cooperative merging mode to represent a cooperative merging process and generates an optimal vehicle merging sequence and time instants when on-ramp CAVs start to adapt their speeds and positions to prepare merging into the target gaps respectively. An operational layer controller is designed based on Model Predictive Control (MPC). It uses a third-order vehicle dynamics model and optimizes desired accelerations for CAVs and the time instants when the on-ramp CAVs initiate the lane-changing executions respectively. Both the tactical layer controller and operational layer controller derive their control commands by minimizing an objective function for different time horizons. The objective function penalizes deviations of CAVs' inter-vehicle gaps to their desired values, relative speeds to their direct predecessors, and actual or desired accelerations, subject to constraints on velocities, actual or desired accelerations, and inter-vehicle gaps. The performance of the proposed hierarchical control framework and a benchmark on-ramp merging method using a first-in-first-out rule to determine the merging sequence is demonstrated under 135 scenarios with different initial conditions, desired time gap settings, and numbers of on-ramp vehicles. The experimental results show the superiority of the hierarchical control approach.
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Gap selection and dynamic speed profiles of interacting vehicles at on-ramps affect the safety and efficiency of highway merging sections. This paper puts forward a hierarchical control approach for Connected Automated Vehicles (CAVs) to achieve efficient and safe merging operations. A tactical layer controller employs a second-order car-following model with a cooperative merging mode to represent a cooperative merging process and generates an optimal vehicle merging sequence and time instants when on-ramp CAVs start to adapt their speeds and positions to prepare merging into the target gaps respectively. An operational layer controller is designed based on Model Predictive Control (MPC). It uses a third-order vehicle dynamics model and optimizes desired accelerations for CAVs and the time instants when the on-ramp CAVs initiate the lane-changing executions respectively. Both the tactical layer controller and operational layer controller derive their control commands by minimizing an objective function for different time horizons. The objective function penalizes deviations of CAVs' inter-vehicle gaps to their desired values, relative speeds to their direct predecessors, and actual or desired accelerations, subject to constraints on velocities, actual or desired accelerations, and inter-vehicle gaps. The performance of the proposed hierarchical control framework and a benchmark on-ramp merging method using a first-in-first-out rule to determine the merging sequence is demonstrated under 135 scenarios with different initial conditions, desired time gap settings, and numbers of on-ramp vehicles. The experimental results show the superiority of the hierarchical control approach.
Conference paper
(2020)
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N. Chen, B. van Arem, M. Wang
This paper aims to optimize on-ramp merging processes for connected automated vehicles by utilizing an existing hierarchical control architecture including a decision-maker and an operational controller. The decision-maker employs surrogate linear models to predict future vehicular acceleration analytically and computes a merging sequence to minimize merging times of on-ramp vehicles. The operational controller is formulated as a model predictive control problem, which utilizes a second-order vehicle dynamics model, and regulates vehicles' accelerations and time instants to execute lateral movements of on-ramp vehicles for the merging processes respectively. Constraints on vehicular acceleration, speed, and inter-vehicle distance are considered by the decision-maker and the operational controller for practical usage. The proposed method to minimize the merging times of on-ramp vehicles and a first-in-first-out method are tested under different initial settings, including initial vehicular speeds, distributions of vehicular positions, and desired time gaps. The simulation results show that the proposed method is superior to the first-in-first-out method widely used in literature in improving merging traffic efficiency. We find that cooperation among vehicles makes the on-ramp vehicles join mainline traffic faster, and the acceptable time gap for merging affect choices of optimal merging sequences.
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This paper aims to optimize on-ramp merging processes for connected automated vehicles by utilizing an existing hierarchical control architecture including a decision-maker and an operational controller. The decision-maker employs surrogate linear models to predict future vehicular acceleration analytically and computes a merging sequence to minimize merging times of on-ramp vehicles. The operational controller is formulated as a model predictive control problem, which utilizes a second-order vehicle dynamics model, and regulates vehicles' accelerations and time instants to execute lateral movements of on-ramp vehicles for the merging processes respectively. Constraints on vehicular acceleration, speed, and inter-vehicle distance are considered by the decision-maker and the operational controller for practical usage. The proposed method to minimize the merging times of on-ramp vehicles and a first-in-first-out method are tested under different initial settings, including initial vehicular speeds, distributions of vehicular positions, and desired time gaps. The simulation results show that the proposed method is superior to the first-in-first-out method widely used in literature in improving merging traffic efficiency. We find that cooperation among vehicles makes the on-ramp vehicles join mainline traffic faster, and the acceptable time gap for merging affect choices of optimal merging sequences.
Journal article
(2018)
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Na Chen, Meng Wang, Tom Alkim, Bart van Arem
Automated vehicles are designed to free drivers from driving tasks and are expected to improve traffic safety and efficiency when connected via vehicle-to-vehicle communication, that is, connected automated vehicles (CAVs). The time delays and model uncertainties in vehicle control systems pose challenges for automated driving in real world. Ignoring them may render the performance of cooperative driving systems unsatisfactory or even unstable. This paper aims to design a robust and flexible platooning control strategy for CAVs. A centralized control method is presented, where the leader of a CAV platoon collects information from followers, computes the desired accelerations of all controlled vehicles, and broadcasts the desired accelerations to followers. The robust platooning is formulated as a Min-Max Model Predictive Control (MM-MPC) problem, where optimal accelerations are generated to minimize the cost function under the worst case, where the worst case is taken over the possible models. The proposed method is flexible in such a way that it can be applied to both homogeneous platoon and heterogeneous platoon with mixed human-driven and automated controlled vehicles. A third-order linear vehicle model with fixed feedback delay and stochastic actuator lag is used to predict the platoon behavior. Actuator lag is assumed to vary randomly with unknown distributions but a known upper bound. The controller regulates platoon accelerations over a time horizon to minimize a cost function representing driving safety, efficiency, and ride comfort, subject to speed limits, plausible acceleration range, and minimal net spacing. The designed strategy is tested by simulating homogeneous and heterogeneous platoons in a number of typical and extreme scenarios to assess the system stability and performance. The test results demonstrate that the designed control strategy for CAV can ensure the robustness of stability and performance against model uncertainties and feedback delay and outperforms the deterministic MPC based platooning control.
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Automated vehicles are designed to free drivers from driving tasks and are expected to improve traffic safety and efficiency when connected via vehicle-to-vehicle communication, that is, connected automated vehicles (CAVs). The time delays and model uncertainties in vehicle control systems pose challenges for automated driving in real world. Ignoring them may render the performance of cooperative driving systems unsatisfactory or even unstable. This paper aims to design a robust and flexible platooning control strategy for CAVs. A centralized control method is presented, where the leader of a CAV platoon collects information from followers, computes the desired accelerations of all controlled vehicles, and broadcasts the desired accelerations to followers. The robust platooning is formulated as a Min-Max Model Predictive Control (MM-MPC) problem, where optimal accelerations are generated to minimize the cost function under the worst case, where the worst case is taken over the possible models. The proposed method is flexible in such a way that it can be applied to both homogeneous platoon and heterogeneous platoon with mixed human-driven and automated controlled vehicles. A third-order linear vehicle model with fixed feedback delay and stochastic actuator lag is used to predict the platoon behavior. Actuator lag is assumed to vary randomly with unknown distributions but a known upper bound. The controller regulates platoon accelerations over a time horizon to minimize a cost function representing driving safety, efficiency, and ride comfort, subject to speed limits, plausible acceleration range, and minimal net spacing. The designed strategy is tested by simulating homogeneous and heterogeneous platoons in a number of typical and extreme scenarios to assess the system stability and performance. The test results demonstrate that the designed control strategy for CAV can ensure the robustness of stability and performance against model uncertainties and feedback delay and outperforms the deterministic MPC based platooning control.
Conference paper
(2018)
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Na Chen, Meng Wang, Tom Alkim, Bart Van Arem
Merging is a challenging task for automated vehicles. This paper proposes a strategy for connected automated vehicles (CAVs) to guide merging on-ramp vehicles efficiently while ensuring safe interactions with the mainline vehicles. Point-mass kinematic models are used to describe 2-D vehicle motion and receding horizon control is used to generate optimal trajectories of interacting vehicles. The strategy determines the optimal merging time instant for merging vehicles and acceleration of all involved vehicles to minimize deviation from the preceding vehicles' speed, deviation from preferred inter-vehicle gaps, accelerations, and the time spent merging. The strategy builds on a pre-determined order of vehicles passing the conflict zone but is not restricted to fixed merging points as previous research assumes. It resembles human-like behavior in the sense that on-ramp vehicles will accept smaller gaps when approaching the end of the acceleration lane. The performance of the strategy is demonstrated in simulations.
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Merging is a challenging task for automated vehicles. This paper proposes a strategy for connected automated vehicles (CAVs) to guide merging on-ramp vehicles efficiently while ensuring safe interactions with the mainline vehicles. Point-mass kinematic models are used to describe 2-D vehicle motion and receding horizon control is used to generate optimal trajectories of interacting vehicles. The strategy determines the optimal merging time instant for merging vehicles and acceleration of all involved vehicles to minimize deviation from the preceding vehicles' speed, deviation from preferred inter-vehicle gaps, accelerations, and the time spent merging. The strategy builds on a pre-determined order of vehicles passing the conflict zone but is not restricted to fixed merging points as previous research assumes. It resembles human-like behavior in the sense that on-ramp vehicles will accept smaller gaps when approaching the end of the acceleration lane. The performance of the strategy is demonstrated in simulations.