An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios

Anderson, Sterling J.; Peters, Steven; Pilutti, Tom E.; Iagnemma, Karl

doi:10.1504/ijvas.2010.035796

Cited by 226 publications

(117 citation statements)

References 39 publications

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“…The current human-in-the-loop control paradigm studies and learns human models and takes action whenever it concludes that the human user is not capable of controlling the system. Anderson et al [1] study obstacle avoidance and lane keeping for semiautonomous cars, which is a common example of human-in-the-loop control. The control input of the semiautonomous vehicle is a weighted sum of control input of driver and control input of the autonomous system with weights representing threat functions that only depend on sideslip angles of the vehicle.…”

Section: Related Workmentioning

confidence: 99%

“…In general, a human-in-the-loop controller, as shown in Figure 1 is a controller consists of three components: an automatic controller, a human operator, and an advisory control mechanism that orchestrates the switching between the auto-controller and the human operator. 1 In this setting, the auto-controller and the human operator can be viewed as two separate controllers, each capable of producing outputs based on inputs from the environment, while the key responsibility of the advisory controller is to determine precisely when the human operator should assume control, while giving her enough time to respond. In this paper, we study the construction of such controller in the context of reactive synthesis from temporal logic.…”

Section: Introductionmentioning

confidence: 99%

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Synthesis for Human-in-the-Loop Control Systems

Sadigh

Sastry

et al. 2014

Lecture Notes in Computer Science

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Abstract-Several control systems in safety-critical applications involve the interaction of an autonomous controller with one or more human operators. Examples include pilots interacting with an autopilot system in an aircraft, and a driver interacting with automated driver-assistance features in an automobile. The correctness of such systems depends not only on the autonomous controller, but also on the actions of the human controller. In this paper, we present a formalism for human-inthe-loop control systems. Particularly, we focus on the problem of synthesizing a semi-autonomous controller from high-level temporal specifications that expect occasional human intervention for correct operation. We present an algorithm for this problem, and demonstrate its operation on problems related to driver assistance in automobiles.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synthesis for Human-in-the-Loop Control Systems

Sadigh

Sastry

et al. 2014

Lecture Notes in Computer Science

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“…However, we have opted for using the driving/braking torques as input, see (10), since this is a quantity that can be controlled in a physical setup of a vehicle.…”

Section: ) Friction Ellipsementioning

confidence: 99%

“…Previous work in the subject of optimal control of vehicles in certain time-critical situations such as T-bone collisions and cornering can be found in, e.g., [7], [8], [9]. In [10], [11], methods for constraintbased trajectory planning for optimal maneuvers are presented. Further, the papers [12], [13] discuss optimal control of overactuated vehicles, where similar optimization tools as those used in the present paper are utilized.…”

Section: Introductionmentioning

confidence: 99%

Models and methodology for optimal vehicle maneuvers applied to a hairpin turn

Berntorp

Olofsson

Bernhardsson

et al. 2013

2013 American Control Conference

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Abstract-There is currently a strongly growing interest in obtaining optimal control solutions for vehicle maneuvers, both in order to understand optimal vehicle behavior and to devise improved safety systems, either by direct deployment of the solutions or by including mimicked driving techniques of professional drivers. However, it is nontrivial to find the right mix of models, formulations, and optimization tools to get useful results for the above purposes. Here, a platform is developed based on a stateof-the-art optimization tool together with adoption of existing vehicle models, where especially the tire models are in focus. A minimum-time formulation is chosen to the purpose of gaining insight in at-the-limit maneuvers, with the overall aim of possibly finding improved principles for future active safety systems. We present optimal maneuvers for different tire models with a common vehicle motion model, and the results are analyzed and discussed. Our main result is that a few-state single-track model combined with different tire models is able to replicate the behavior of experienced drivers. Further, we show that the different tire models give quantitatively different behavior in the optimal control of the vehicle in the maneuver.

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“…: grid-based approaches [15], planning using motion primitives [1], rapidly-exploring random trees [2]- [4], and road maps [5]- [9]); 2) planning in continuous space (e.g. optimal control, model predictive control [10]- [13], and elastic bands [14] for collision-free trajectory planning for mobile robots can be found in [16].…”

Section: Introductionmentioning

confidence: 99%

Fail-safe motion planning of autonomous vehicles

Magdici

Althoff

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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Abstract-Formally verified methods for motion planning are required in order to guarantee safety for autonomous vehicles. In particular, we consider trajectory generation by considering the most probable trajectory of other traffic participants. However, if the surrounding vehicles perform unexpected maneuvers, a collision might be inevitable. In this paper, a fail-safe motion planner is developed, which generates optimal trajectories, yet guarantees safety at all times. Safety is achieved by maintaining an emergency maneuver which can safely bring the host vehicle to a stop while avoiding any collision. The emergency maneuver is computed by considering for a given time horizon the occupancy prediction which encloses all possible trajectories of the other traffic participants. The performance of the approach is evaluated through simulation against real traffic data.

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An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios

Cited by 226 publications

References 39 publications

Synthesis for Human-in-the-Loop Control Systems

Synthesis for Human-in-the-Loop Control Systems

Models and methodology for optimal vehicle maneuvers applied to a hairpin turn

Fail-safe motion planning of autonomous vehicles

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