Dynamic Modeling and Control of High-Speed Automated Vehicles for Lane Change Maneuver

Liu, Kai; Gong, Jianya; Kurt, Arda; Chen, Huiyan; Özgüner, Ü.

doi:10.1109/tiv.2018.2843177

Cited by 104 publications

(42 citation statements)

References 27 publications

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“…In implementations on full size vehicles, real-time operation with longer planning horizons has been achieved by using MPC to compute the optimal steering inputs only after the longitudinal inputs are determined by another means. Liu et al perform lane change maneuvers by first computing a velocity profile that will place the vehicle in a gap in traffic before performing the lane change with a steering only MPC [15]. In the approaches of Funke et al [16] and Gutjahr et al [17], the vehicle tracks a desired velocity profile while avoiding obstacles using steering only.…”

Section: A Related Workmentioning

confidence: 99%

From the Racetrack to the Road: Real-Time Trajectory Replanning for Autonomous Driving

Subosits

Gerdes

2019

IEEE Trans. Intell. Veh.

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In emergency situations, autonomous vehicles will be forced to operate at their friction limits in order to avoid collisions. In these scenarios, coordinating the planning of the vehicle's path and speed gives the vehicle the best chance of avoiding an obstacle. Fast reaction time is also important in an emergency, but approaches to the trajectory planning problem based on nonlinear optimization are computationally expensive. This paper presents a new scheme that simultaneously modifies the desired path and speed profile for a vehicle in response to the appearance of an obstacle, significant tracking error, or other environmental change. By formulating the trajectory optimization problem as a quadratically constrained quadratic program, solution times of less than 20 ms are possible even with a 10-s planning horizon. A simplified point mass model is used to describe the vehicle's motion, but the incorporation of longitudinal weight transfer and road topography means that the vehicle's acceleration limits are modeled more accurately than in comparable approaches. Experimental data from an autonomous vehicle in two scenarios demonstrate how the trajectory planner enables the vehicle to avoid an obstacle even when the obstacle appears suddenly and the vehicle is already operating near the friction limits.

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Section: A Related Workmentioning

confidence: 99%

From the Racetrack to the Road: Real-Time Trajectory Replanning for Autonomous Driving

Subosits

Gerdes

2019

IEEE Trans. Intell. Veh.

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“…Regarding the trajectory planning methods, current studies mainly contain two kinds of models [12], [13]. Some research tends to consider higher-level planning that considers longitudinal velocity change [14]- [16] while neglecting a vehicle's dynamic characteristics.…”

Section: A State-of-the-art Review and Challengesmentioning

confidence: 99%

A Novel Trajectory Planning Method for Automated Vehicles Under Parameter Decision Framework

et al. 2019

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Decision and control in all stack scenarios comprise a key issue in the design of automated vehicle control systems. Thus, in higher level, automated vehicles, the decision and the form of the decision should be able to adapt to diverse, changeable, and complex scenarios, which increase the complexity of trajectory planning. In this paper, a parameter decision framework in which the decision is described with key parameters, rather than specific behaviors, such as lane-changing or car-following, is considered. Under this framework, a novel trajectory planning method is proposed to implement behavior with integrated longitudinal and lateral control, in which a nonlinear motion control model is established. The nonlinear model predictive control (NMPC) method with terminal constraints without a predefined path form is applied, which presents more flexibility for changeable decisions. Both the trajectory planning controller and the overall framework are verified by simulation. The results show the validity of the controller and the framework. INDEX TERMS Model predictive control, trajectory planning, decision-making, integrated longitudinal and lateral control.

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“…However, limited by the local shapes of interpolated curves, it is easy for the planner to get into trouble when the vehicle is close to the surrounding obstacles, which we call it a near-obstacle planning trouble, as is shown in Figure 1. For the path tracking and lateral stability controller, some advanced control methods have been developed, such as pure pursuit tracking control and model predictive control [20,21], robust control [22][23][24], fuzzy control [25], sliding mode control [26][27][28], adaptive control [28,29], non-smooth control [30], disturbance decoupling control [31], other nonlinear control [32], which have shown promising perspective in vehicle engineering application. In this study, we consider that our main objective tends to develop trajectory planning technique for on-road autonomous driving, hence the path tracking control is simplified, we employ a modified pure pursuit tracking controller for forward simulation to obtain a feasible control sequence of the vehicle and further smooth the path simultaneously.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Motion Planning Technique for On-Road Autonomous Driving

et al. 2021

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Dynamic Modeling and Control of High-Speed Automated Vehicles for Lane Change Maneuver

Cited by 104 publications

References 27 publications

From the Racetrack to the Road: Real-Time Trajectory Replanning for Autonomous Driving

From the Racetrack to the Road: Real-Time Trajectory Replanning for Autonomous Driving

A Novel Trajectory Planning Method for Automated Vehicles Under Parameter Decision Framework

An Adaptive Motion Planning Technique for On-Road Autonomous Driving

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