Semiautonomous Vehicular Control Using Driver Modeling

Shia, Victor; Gao, Yiqi; Vasudevan, Ram; Campbell, Katherine Driggs; Lin, Theresa; Borrelli, Francesco; Bajcsy, Růžena

doi:10.1109/tits.2014.2325776

Cited by 97 publications

(58 citation statements)

References 34 publications

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“…A typical assumption for intelligent vehicles has been to consider the velocity of the vehicle as given and optimize only over the steering angle, thus rendering the optimization problem tractable. For example, Shia et al (26) minimized the difference in steering wheel angle from the human predicted control input, which is necessary to achieve safe trajectories. Alternatively, Erlien et al (27) defined vehicle stability and environmental envelopes to supply safe steering commands in a discretized environment, considering the vehicle speed to be constant and solving a receding-horizon convex optimization.…”

Section: Parallel Autonomymentioning

confidence: 99%

Planning and Decision-Making for Autonomous Vehicles

Schwarting

Alonso–Mora

Rus

2018

Annu. Rev. Control Robot. Auton. Syst.

711

378

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In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and systemcompliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, such as interactive planning and end-to-end learning, open up questions regarding safety and reliability that need to be addressed. In this survey, we emphasize recent approaches for integrated perception and planning and for behavior-aware planning, many of which rely on machine learning. This raises the question of verification and safety, which we also touch upon. Finally, we discuss the state of the art and remaining challenges for managing fleets of autonomous vehicles. 8.1

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Section: Parallel Autonomymentioning

confidence: 99%

Planning and Decision-Making for Autonomous Vehicles

Schwarting

Alonso–Mora

Rus

2018

Annu. Rev. Control Robot. Auton. Syst.

711

378

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“…However then it follows from the properties of y u ξ that there exist (τ ,d) ∈ HT (D) andχ ∈ Hd π (ξ) such that for all t ∈ [0,t], ȳ(t) − y u ξ (t) ≤ . This implies thatȳ(t) ∈ B which contradict (12) and thus achieves the proof of (11). To show the opposite inclusion it suffices by Lemma 3 to show that ξ ∈ X y u ξ (R + ) ∩ B = ∅ ⊂ {ξ ∈ X | y(R + ) ∩ B = ∅ ∀χ ∈ H π (ξ)} , where π is as in Definition 11.…”

Section: Proof Of Theoremmentioning

confidence: 65%

“…Ideally, provably safe controllers should also be least restrictive, which means in the context of a driver-assist system that the controller constrains the possible actions of the human driver as little as possible. Due to the computational complexity of the task, the design of provably safe, least restrictive controllers remains a challenge and can in general only be done approximately, see for instance [9,3,11]. However it has been shown that a number of ground transportation systems have the so-called inputoutput order preserving property, in which case exact solutions are possible, see [4,12,5] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Controller design under safety specifications for a class of bounded hybrid automata

Hoehener

Vecchio

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Motivated by driver-assist systems that warn the driver before taking control action, we study the safety problem for a class of bounded hybrid automata. We show that for this class there exists a least restrictive safe feedback controller that has a simple structure and can be computed efficiently online. The theoretical results are then used to design driver-assist systems for rear-end and merging collision scenarios.

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“…[20] directly minimized the difference from the human predicted control input necessary to achieve safe trajectories, and [21] minimized the difference in steering wheel angle. [22] minimize the deviation from desired front wheel lateral force with an additional discount factor with increasing time.…”

Section: B Shared Control Of Intelligent Vehiclesmentioning

confidence: 99%

Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model

Schwarting

Alonso–Mora

Paull

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-High-end vehicles are already equipped with safety systems, such as assistive braking and automatic lane following, enhancing vehicle safety. Yet, these current solutions can only help in low-complexity driving situations. In this work, we introduce a Parallel Autonomy, or shared control, framework that computes safe trajectories for an automated vehicle, based on human inputs. We minimize the deviation from the human inputs while ensuring safety via a set of collision avoidance constraints. Our method achieves safe motion even in complex driving scenarios, such as those commonly encountered in an urban setting. We introduce a receding horizon planner formulated as Nonlinear Model Predictive Control (NMPC), which includes analytic descriptions of road boundaries and the configuration and future uncertainties of other road participants. The NMPC operates over both steering and acceleration simultaneously. We introduce a nonslip model suitable for handling complex environments with dynamic obstacles, and a nonlinear combined slip vehicle model including normal load transfer capable of handling static environments. We validate the proposed approach in two complex driving scenarios. First, in an urban environment that includes a left-turn across traffic and passing on a busy street. And second, under snow conditions on a race track with sharp turns and under complex dynamic constraints. We evaluate the performance of the method with various human driving styles. We consequently observe that the method successfully avoids collisions and generates motions with minimal intervention for Parallel Autonomy. We note that the method can also be applied to generate safe motion for fully autonomous vehicles.

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Semiautonomous Vehicular Control Using Driver Modeling

Cited by 97 publications

References 34 publications

Planning and Decision-Making for Autonomous Vehicles

Planning and Decision-Making for Autonomous Vehicles

Controller design under safety specifications for a class of bounded hybrid automata

Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model

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