Design and implementation of human driving data–based active lane change control for autonomous vehicles

Chae, Heung-Seok; Jeong, Yonghwan; Lee, Hojun; Park, Jongcherl

doi:10.1177/0954407020947678

Cited by 17 publications

(6 citation statements)

References 44 publications

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“…The perception module provides the states of the detected vehicle tracks including position, velocity and acceleration [26]. In the case of basic lane keeping and clearance control maneuvers, the corresponding functions have been successfully performed only with the aforementioned target state information [27]. However, in order to plan the avoidance behavior which will be presented in section IV, it is necessary to accurately measure how much the obstacle occupies the lateral space of the driving lane as well as the states of the LiDAR track.…”

Section: A Safe Drivable Envelope-based Environment Representationmentioning

confidence: 99%

Trajectory Planning and Control of Autonomous Vehicles for Static Vehicle Avoidance in Dynamic Traffic Environments

et al. 2023

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This paper presents a trajectory planning and control algorithm of autonomous vehicles for static traffic agent avoidance in multi vehicle urban environments. In urban autonomous driving, the subject vehicle encounters diverse traffic scenes including lane changing, intersection driving, and illegally parked static vehicle avoidance. Among these, dealing with illegally parked static target vehicle is a major challenge to urban autonomous driving due to large velocity difference between ego and target vehicles and interactions with surrounding vehicles. In order to tackle this problem, we introduce a decision making and motion planning framework for static vehicle avoidance considering both the preceding static vehicles and surrounding vehicles. Among the surrounding vehicles, the set of objects with potential collision risk is selected based on the lane boundaries and road geometry. Then, the driving status of the selected target vehicles are classified as normal driving vehicles or parked vehicles based on their longitudinal speed, lateral position and lateral space occupancy. For the preceding parked vehicles, the motion planner generates lateral and longitudinal evasive motion, by taking side lane traffic flow and risk into account. The desired motion is executed by applying optimized control inputs computed by lateral and longitudinal model predictive controllers. The performance validation of the proposed algorithm has been conducted with actual autonomous test vehicles. The test results confirmed that the proposed algorithm can successfully perform evasive maneuvers on urban roads to ensure safety and mitigate collision risk with the surrounding traffic agents.

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Section: A Safe Drivable Envelope-based Environment Representationmentioning

confidence: 99%

Trajectory Planning and Control of Autonomous Vehicles for Static Vehicle Avoidance in Dynamic Traffic Environments

et al. 2023

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“…The remaining time t dart to arrive at the virtual conflict point of the target vehicle is calculated by assuming the dart-out velocity v dart of the target vehicle and dividing the d dart by v dart . v dart is defined as estimated traffic flow velocity [44]. t dart is the remaining time to decelerate the autonomous vehicle to avoid a collision with an approaching target from the blind spots.…”

Section: Target State Decision For Approach Motionmentioning

confidence: 99%

Proactive Motion Planning for Uncontrolled Blind Intersections to Improve the Safety and Traffic Efficiency of Autonomous Vehicles

Park

Jeong

2022

Applied Sciences

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For the last two decades, autonomous vehicles have been proposed and developed to extend the operational design domain from the motorway to urban environments. However, there have been few studies on autonomous driving for uncontrolled and blind intersections. This paper presents a proactive motion planning algorithm to enhance safety and traffic efficiency simultaneously for autonomous driving in uncontrolled blind intersections. The target states of approach motion are decided based on the field of view of the laser scanner and the pre-defined intersection map with connectivity information. The model predictive controller is used to follow the target states and determine the longitudinal motion of an autonomous vehicle. A Monte Carlo simulation with a case study was conducted to evaluate the performance of the proposed proactive motion planner. The simulation results show that the risk caused by approaching vehicles from the occluded region is properly managed. In addition, the traffic flow is improved by reducing the required time to cross the intersections.

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“…An MPC problem is designed to deal with the requirements mentioned above. The concept in [42] is utilized in the predictive control against the cut-in vehicle. To reflect the system dynamics to the MPC, a point mass model is used as a plant model as defined below:…”

Section: Mpc-based Motion Planning and Controlmentioning

confidence: 99%

Interaction-Aware Probabilistic Trajectory Prediction of Cut-In Vehicles Using Gaussian Process for Proactive Control of Autonomous Vehicles

et al. 2021

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This paper presents a probabilistic trajectory prediction of cut-in vehicles exploiting the information of interacting vehicles. First, a probability distribution of behavioral parameters, which represents the characteristics of lane-change motion, is obtained via Gaussian Process Regression (GPR). For this purpose, Gaussian Process (GP) models are trained using real-world trajectories of lane-changing vehicles and adjacent vehicles. Subsequently, the future states of the lane-change vehicle are probabilistically estimated using a path-following model, which introduces virtual measurements based on the information of behavioral parameters. The proposed predictor is applied to the motion planning and control of autonomous vehicles. A Model Predictive Control (MPC) is designed to achieve predictive maneuvering of autonomous vehicles against cut-in preceding vehicles. The proposed predictor has been evaluated in terms of its prediction accuracy. Also, the performance of the proposed predictor-based control has been validated via computer simulations and autonomous driving vehicle tests. Compared to conventional prediction methods, it is shown that the interaction-aware proposed predictor provides improved prediction of cut-in vehicles' motion in multi-vehicle scenarios. Furthermore, the control results indicate that the proposed predictor helps the autonomous vehicle to reduce the control effort and improve ride quality for passengers in cut-in scenarios, while guaranteeing safety.

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Design and implementation of human driving data–based active lane change control for autonomous vehicles

Cited by 17 publications

References 44 publications

Trajectory Planning and Control of Autonomous Vehicles for Static Vehicle Avoidance in Dynamic Traffic Environments

Trajectory Planning and Control of Autonomous Vehicles for Static Vehicle Avoidance in Dynamic Traffic Environments

Proactive Motion Planning for Uncontrolled Blind Intersections to Improve the Safety and Traffic Efficiency of Autonomous Vehicles

Interaction-Aware Probabilistic Trajectory Prediction of Cut-In Vehicles Using Gaussian Process for Proactive Control of Autonomous Vehicles

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