Intention estimation for ramp merging control in autonomous driving

Dong, Chiyu; Dolan, John M.; Litkouhi, Bakhtiar

doi:10.1109/ivs.2017.7995935

Cited by 87 publications

(45 citation statements)

References 9 publications

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“…improvement in performance can be gained when the planning algorithm has access to information about the driver internal state in lane changing scenarios. Previous work address the problem of modeling interactions between traffic participants using data-driven approaches, probabilistic models, inverse reinforcement learning, rule-based methods, or game theoretic frameworks [2], [4], [5], [8], [9]. Inverse reinforcement learning techniques and game theoretic frameworks are generally too computationally expensive to be used in an online planning algorithm considering more than two traffic participants [5], [9].…”

Section: Introductionmentioning

confidence: 99%

Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic

Bouton

Nakhaei

Fujimura

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Decision making in dense traffic can be challenging for autonomous vehicles. An autonomous system only relying on predefined road priorities and considering other drivers as moving objects will cause the vehicle to freeze and fail the maneuver. Human drivers leverage the cooperation of other drivers to avoid such deadlock situations and convince others to change their behavior. Decision making algorithms must reason about the interaction with other drivers and anticipate a broad range of driver behaviors. In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels. We enhanced the performance of traditional reinforcement learning algorithms by maintaining a belief over the level of cooperation of other drivers. We show that our agent successfully learns how to navigate a dense merging scenario with less deadlocks than with online planning methods.

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Section: Introductionmentioning

confidence: 99%

Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic

Bouton

Nakhaei

Fujimura

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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“…In this work, our goal is the recognition of driving intention and the prediction of driving behaviors on the highway. Different methodologies have been proposed to predict driving behaviors on the intersection [1]- [4], ramp [5]- [7], and highway [8]- [10]. Transition of driving behavior on the intersection is represented by FSM and the future driving behavior is predicted in [1].…”

Section: Introductionmentioning

confidence: 99%

Driving Intention Recognition and Lane Change Prediction on the Highway

Han

Jing

Özgüner

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the host-vehicle. The framework consists of a driving characteristic estimator and a driving behavior predictor. A driver's implicit driving characteristic information is uniquely determined and detected by proposed the online-estimator. Neural-network based behavior predictor is developed and validated by testing with the real naturalistic traffic data from Next Generation Simulation (NGSIM), which demonstrates the effectiveness in identifying the driving characteristics and transforming into accurate behavior prediction in real-world traffic situations.

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“…In addition to the advancement on algorithmic aspect of autonomous driving, these competitions also lead to the advancement on sensor side, with development of multi-beam lidar sensors such as the Velodyne range of devices [4] conceived and prototyped during the Grand Challenge [5]. Since then, a significant amount of research has been carried out addressing different aspects of autonomous driving such as object detection [6,7], localisation [8], tracking [9], intention estimation [10], as well as end-to-end deep-learning based approaches (e.g., [11]). In addition to the research explicitly addressing autonomous driving, a lot of closely-related research has also been carried out in the area of Advanced Driver Assistance Systems (ADAS); for example, Martinez et al [12] investigated approaches for driving style recognition.…”

Section: Introductionmentioning

confidence: 99%

Predicting Agent Behaviour and State for Applications in a Roundabout-Scenario Autonomous Driving

Muhammad

Åstrand

2019

Sensors

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As human drivers, we instinctively employ our understanding of other road users’ behaviour for enhanced efficiency of our drive and safety of the traffic. In recent years, different aspects of assisted and autonomous driving have gotten a lot of attention from the research and industrial community, including the aspects of behaviour modelling and prediction of future state. In this paper, we address the problem of modelling and predicting agent behaviour and state in a roundabout traffic scenario. We present three ways of modelling traffic in a roundabout based on: (i) the roundabout geometry; (ii) mean path taken by vehicles inside the roundabout; and (iii) a set of reference trajectories traversed by vehicles inside the roundabout. The roundabout models are compared in terms of exit-direction classification and state (i.e., position inside the roundabout) prediction of query vehicles inside the roundabout. The exit-direction classification and state prediction are based on a particle-filter classifier algorithm. The results show that the roundabout model based on set of reference trajectories is better suited for both the exit-direction and state prediction.

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Intention estimation for ramp merging control in autonomous driving

Cited by 87 publications

References 9 publications

Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic

Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic

Driving Intention Recognition and Lane Change Prediction on the Highway

Predicting Agent Behaviour and State for Applications in a Roundabout-Scenario Autonomous Driving

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