The Foresighted Driver Model

Eggert, Julian; Damerow, Florian; Klingelschmitt, Stefan

doi:10.1109/ivs.2015.7225706

Cited by 40 publications

(21 citation statements)

References 8 publications

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“…A model based approach for lane change decision-making is presented in [10]. Therein, the foresighted driver model (FDM) [11] is utilized. The FDM is able to incorporate utility and risk terms to be fused to a final cost functional in the first step.…”

Section: B Related Workmentioning

confidence: 99%

A Model Based Motion Planning Framework for Automated Vehicles in Structured Environments

Graf

Speidel

Dietmayer

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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A main difficulty in autonomous driving is the assurance of maneuver acceptability by other traffic participants. Thus, knowledge about social interaction needs to be incorporated into the motion planning process. In this paper we present a model based framework to verify the acceptance of considered maneuvers and to plan social compliant motions. Therefore, we fuse two powerful approaches, one for decisionmaking and one for planning and show how the methods benefit from each other. Our method adheres to the classical structure of decision-making with subsequent trajectory planning and is consistent in the sense that both components are subject on the same, identical parametrized driver model. The overall method is real-time capable and the resulting trajectories adhere to kinematic constraints. Thus, the approach is applicable in realworld systems.

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Section: B Related Workmentioning

confidence: 99%

A Model Based Motion Planning Framework for Automated Vehicles in Structured Environments

Graf

Speidel

Dietmayer

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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“…We therefore utilize the well-known Intelligent Driver Model (IDM), which mimics human driving behavior for follow and free driving scenarios. Another alternative would be to use the Foresighted Driver Model [23].…”

Section: Situation Predictionmentioning

confidence: 99%

Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving

Taş

Stiller

2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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Adverse weather conditions and occlusions in urban environments result in impaired perception. The uncertainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This paper focuses on motion planning given an uncertain environment model with occlusions. We present a method to remain collision free for the worst-case evolution of the given scene. We define criteria that measure the available margins to a collision while considering visibility and interactions and consequently integrate conditions that apply these criteria into an optimization-based motion planner. We show the generality of our method by validating it in several distinct urban scenarios.

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“…Another driver model is the Foresighted Driver Model presented in [2] where drivers are modeled with a risk vs utility behavior and risk is predicted from future possible collisions. In this work we will use the IDM for modeling and prediction of future trajectories for other vehicles due to its simplicity and its ability to capture real driver behavior.…”

Section: A Related Workmentioning

confidence: 99%

“…The first two authors contributed equally to this work. 1 Division of Automatic Control, Linköping University, Sweden, (e-mail: niclas.evestedt@liu.se, daniel.axehill@liu.se) 2 Computer Vision and Active Perception Lab, KTH, Stockholm, Sweden (e-mail: eward@kth.se, johnf@kth.se) Fig. 1: Merging scenario in a T-junction.…”

Section: Introductionmentioning

confidence: 99%

Interaction aware trajectory planning for merge scenarios in congested traffic situations

Evestedt

Ward²,

Folkesson³

et al. 2016

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

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Abstract-In many traffic situations there are times where interaction with other drivers is necessary and unavoidable in order to safely progress towards an intended destination. This is especially true for merge manoeuvres into dense traffic, where drivers sometimes must be somewhat aggressive and show the intention of merging in order to interact with the other driver and make the driver open the gap needed to execute the manoeuvre safely. Many motion planning frameworks for autonomous vehicles adopt a reactive approach where simple models of other traffic participants are used and therefore need to adhere to large margins in order to behave safely. However, the large margins needed can sometimes get the system stuck in congested traffic where time gaps between vehicles are too small. In other situations, such as a highway merge, it can be significantly more dangerous to stop on the entrance ramp if the gaps are found to be too small than to make a slightly more aggressive manoeuvre and let the driver behind open the gap needed. To remedy this problem, this work uses the Intelligent Driver Model (IDM) to explicitly model the interaction of other drivers and evaluates the risk by their required deceleration in a similar manner as the Minimum Overall Breaking Induced by Lane change (MOBIL) model that has been used in large scale traffic simulations before. This allows the algorithm to evaluate the effect on other drivers depending on our own trajectory plans by simulating the nearby traffic situation. Finding a globally optimal solution is often intractable in these situations so instead a large set of candidate trajectories are generated that are evaluated against the traffic scene by forward simulations of other traffic participants. By discretization and using an efficient trajectory generator together with efficient modelling of the traffic scene real-time demands can be met.

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The Foresighted Driver Model

Cited by 40 publications

References 8 publications

A Model Based Motion Planning Framework for Automated Vehicles in Structured Environments

A Model Based Motion Planning Framework for Automated Vehicles in Structured Environments

Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving

Interaction aware trajectory planning for merge scenarios in congested traffic situations

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