Analytic collision anticipation technology considering agents' future behavior

Choi, Jin Hwan; Eoh, Gyuho; Kim, Jimin; Yoon, Younghwan; Park, Junghee; Lee, Beom H.

doi:10.1109/iros.2010.5651625

Cited by 11 publications

(8 citation statements)

References 20 publications

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“…Much research has aimed to anticipate future intentions of other traffic agents [17]. The solutions proposed range from deterministic models [18] to different probabilistic models [19], such as Kalman Filters (KF) [20], Dynamic Bayesian Networks (DBN), and Hierarchical Dynamic Bayesian Networks (HDBN) [21] or Gaussian Process (GP) regression [22]. Most statistic observers provide observation with a probability value or a probability distribution.…”

Section: Related Workmentioning

confidence: 99%

Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework

López

Sánchez-Vilariño

Sanz

et al. 2020

Sensors

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Most autonomous car control frameworks are based on a middleware layer with several independent modules that are connected by an inter-process communication mechanism. These modules implement basic actions and report events about their state by subscribing and publishing messages. Here, we propose an executive module that coordinates the activity of these modules. This executive module uses hierarchical interpreted binary Petri nets (PNs) to define the behavior expected from the car in different scenarios according to the traffic rules. The module commands actions by sending messages to other modules and evolves its internal state according to the events (messages) received. A programming environment named RoboGraph (RG) is introduced with this architecture. RG includes a graphical interface that allows the edition, execution, tracing, and maintenance of the PNs. For the execution, a dispatcher loads these PNs and executes the different behaviors. The RG monitor that shows the state of all the running nets has proven to be very useful for debugging and tracing purposes. The whole system has been applied to an autonomous car designed for elderly or disabled people.

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Section: Related Workmentioning

confidence: 99%

Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework

López

Sánchez-Vilariño

Sanz

et al. 2020

Sensors

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“…The problem is complex; it is inherently probabilistic because perfect knowledge of the future is impossible, and it requires not only a model of the dynamics of the object being anticipated, but also of the controller of that object. Some works simplify the problem by making deterministic predictions of object behavior (Choi et al., ; Ohki et al., ; Petti & Fraichard, ), and these approaches work well when the behavior of the dynamic objects is known or communicated to the robot, as in a cooperative scenario.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation and Formal Analysis of High‐Level Tasks with Dynamic Obstacle Anticipation on a Full‐Sized Autonomous Vehicle

Johnson

Havlak

Kress-Gazit

et al. 2017

Journal of Field Robotics

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Certifying the behavior of autonomous systems is essential to the development and deployment of systems in safety‐critical applications. This paper presents an approach to using a correct‐by‐construction controller with the probabilistic results of dynamic obstacle anticipation, and validates the approach with experimental data obtained from Cornell's full‐scale autonomous vehicle. The obstacle anticipation (used to calculate the probability of collision with dynamic obstacles around the vehicle) is abstracted to a set of Boolean observations, which are then used by the synthesized controller (a state machine generated from temporal logic task specifications). The obstacle anticipation, sensor abstraction, and synthesized controller are implemented on a full‐scale autonomous vehicle, and experimental data are collected and compared with a formal analysis of the probabilistic behavior of the system. A comparison of the results shows good agreement between the formal analysis and the experimental results.

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“…Interesting approaches to determine the future behaviour of moving obstacles also been proposed. One of them is the definition of a discrete number of hypothesis of movement, each one related with a possible conduct of the object [5], [6], and the 3-D Triangular Collision Object (TCO) [13]. However, these methods do not contemplate the influence of the environment over the movement of the obstacles.…”

Section: Introductionmentioning

confidence: 99%

Navigation assistance system based on collision risks estimation using depth sensors

Zarricueta

Cheein

2015

2015 4th International Conference on Systems and Control (ICSC)

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Recently, the use of assistive vehicles in industrial or daily day tasks started to grow rapidly. Therefore, it is important to guarantee safety to the robot and to any other moving element in the environment (either people, animals or other robots). In this work, we develop and implement a navigation assistive system based on collision risk estimation using depth sensors. Speed and steering constraints are applied to semi-autonomous assistance vehicles to avoid hazardous situations and to improve the users welfare. We calculate a collision risk indicator based on the tracking of moving elements from the scene, by means of a visual tracking approach and a proposed motion model. The performance of the system is tested in selected situations. Furthermore, the motion model associated with people is empirically validated. Finally, the simulation results included here, show the effectiveness of the system in reducing the imminent collision risk up to 90%, without imposing drastic decisions over the vehicle movement.

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Analytic collision anticipation technology considering agents' future behavior

Cited by 11 publications

References 20 publications

Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework

Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework

Experimental Evaluation and Formal Analysis of High‐Level Tasks with Dynamic Obstacle Anticipation on a Full‐Sized Autonomous Vehicle

Navigation assistance system based on collision risks estimation using depth sensors

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