Predictive Control of Autonomous Ground Vehicles With Obstacle Avoidance on Slippery Roads

Gao, Yiqi; Lin, Theresa; Borrelli, Francesco; Tseng, Eric; Hrovat, Davor

doi:10.1115/dscc2010-4263

Cited by 182 publications

(142 citation statements)

References 11 publications

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“…The challenge of these approaches is in deciding the adequate assumptions and abstractions for the domain-specific knowledge. In predictive control [18][19][20], for example, the controller decides the value of the control signal based on the predicted, rather than the current, values of parameters pertinent to the state of the car and its environment. The prediction could be seen as approximating the values of these parameters from their current values, their rate of change, and the prediction time that corresponds to the latencies in the control loop.…”

Section: Introductionmentioning

confidence: 99%

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Алексеева¹,

Tanev²,

Shimohara³

2018

Algorithms

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Abstract:The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions.

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Section: Introductionmentioning

confidence: 99%

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Алексеева¹,

Tanev²,

Shimohara³

2018

Algorithms

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“…In [5], the MPC problem has been formulated as a quadratic program (QP) by limiting the intervention to the steering, and linearizing the vehicle dynamics around a constant vehicle speed and small slip angles. In [8]- [10], the authors address the problem of integrated braking and steering control by using a hierarchical control architecture. A high-level controller generates an obstacle-free trajectory, while a low level controller tracks this planned trajectory.…”

Section: Introductionmentioning

confidence: 99%

“…where C * U and C * L are functions of the parameters in (8). The corresponding slip angle intervals in which the upper and lower approximations are valid are denoted as…”

mentioning

confidence: 99%

Linear model predictive control for lane keeping and obstacle avoidance on low curvature roads

Turri

Carvalho

Tseng

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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115

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Abstract-This paper presents a control architecture based on a linear MPC formulation that addresses the lane keeping and obstacle avoidance problems for a passenger car driving on low curvature roads. The proposed control design decouples the longitudinal and lateral dynamics in two successive stages. First, plausible braking or throttle profiles are defined over the prediction horizon. Then, based on these profiles, linear timevarying models of the vehicle lateral dynamics are derived and used to formulate the associated linear MPC problems. The solutions of the optimization problems are used to determine for every time step, the optimal braking or throttle command and the corresponding steering angle command. Simulations show the ability of the controller to overcome multiple obstacles and keep the lane. Experimental results on an autonomous passenger vehicle driving on slippery roads show the effectiveness of the approach.

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“…In [7], a hierarchical approach for automated highway driving was introduced, where the high-level control uses a point-mass representation of the vehicle. This might work well for steady-state conditions.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical predictive control for ground-vehicle maneuvering

Berntorp

Magnusson

2015

2015 American Control Conference (ACC)

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Abstract-This paper presents a hierarchical approach to feedback-based trajectory generation for improved vehicle autonomy. Hierarchical vehicle-control structures have been used before-for example, in electronic stability control systems, where a low-level control loop tracks high-level references. Here, the control structure includes a nonlinear vehicle model already at the high level to generate optimization-based references. A nonlinear model-predictive control (MPC) formulation, combined with a linearized MPC acting as a backup controller, tracks these references by allocating torque and steer commands. With this structure the two control layers have a physical coupling, which makes it easier for the low-level loop to track the references. Simulation results show improved performance over an approach based on linearized MPC, as well as feasibility for online implementations.

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Predictive Control of Autonomous Ground Vehicles With Obstacle Avoidance on Slippery Roads

Cited by 182 publications

References 11 publications

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Linear model predictive control for lane keeping and obstacle avoidance on low curvature roads

Hierarchical predictive control for ground-vehicle maneuvering

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