Multi-Level Bayesian Decision-Making for Safe and Flexible Autonomous Navigation in Highway Environment

Iberraken, Dimia; Adouane, Lounis; Denis, Dieumet

doi:10.1109/iros.2018.8593565

Cited by 26 publications

(19 citation statements)

References 10 publications

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“…In addition to that, the ego-vehicle provides information about its own odometery. Finally, these datasets will be made available for researchers 1 . In the following, they will be referred to as "2-lanes" for two lanes road and "3-lanes" for three lanes road (1000 images frames).…”

Section: Real-world Experimental Resultsmentioning

confidence: 99%

“…The ego-localization of a vehicle is a mandatory component for a safe autonomous driving. Direct applications range from the decision-making system [1] regarding the decisions to make in order to keep lane or to change lane, to navigation: including path planning and vehicle control [2]. Ultimately, the lane/road understanding demands in terms of precision and false alarm rate [3] vary from one application to another.…”

Section: Introductionmentioning

confidence: 99%

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End-to-End Probabilistic Ego-Vehicle Localization Framework

Kasmi

Laconte

Aufrère

et al. 2021

IEEE Trans. Intell. Veh.

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Locating the vehicle in its road is a critical part of any autonomous vehicle system and has been subject to different research topics. In most works presented in the literature, ego-localization is split into three parts: Road level-localization consisting in the road on which the vehicle travels, Lane level localization which is the lane on which the vehicle travels, and Ego lane level localization being the lateral position of the vehicle in the ego-lane. For each part, several researches have been conducted. However, the relationship between the different parts has not been taken into consideration. Through this work, an end-to-end ego-localization framework is introduced with two main novelties. The first one is the proposition of a complete solution that tackles every part of the ego-localization. The second one lies in the information-driven approach used. Indeed, we use prior about the road structure from a digital map in order to reduce the space complexity for the recognition process. Besides, several fusion framework techniques based on Bayesian Network and Hidden Markov Model are elaborated leading to an ego-localization method that is, to a large extent, robust to erroneous sensor data. The robustness of the proposed method is proven on different datasets in varying scenarios.

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Section: Real-world Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

End-to-End Probabilistic Ego-Vehicle Localization Framework

Kasmi

Laconte

Aufrère

et al. 2021

IEEE Trans. Intell. Veh.

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“…Vehicle localization is one of the critical features of Advanced driver assistance Systems (ADAS) [1]. These systems will recommand lane change [2] to drivers in order to maintain the driver's safety [3]. Therefore, an accurate but also a reliable ego-vehicle localization is needed.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic framework for ego-lane determination

Kasmi

Denis²,

Aufrère

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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In this paper we propose a method for accurate ego-lane localization using camera images, on-board sensors and lanes number information from OpenStreetMap (OSM). The novelty relies in the probabilistic framework developed, as we introduce a modular Bayesian Network (BN) to infer the ego-lane position from multiple inaccurate information sources. The flexibility of the BN is proven, by first, using only information from surrounding lane-marking detections and second, by adding adjacent vehicles detection information. Afterward, we design a Hidden Markov Model (HMM) to temporary filter the outcome of the BN using the lane change information. The effectiveness of the algorithm is first verified on recorded images of national highway in the region of Clermont-Ferrand. Then, the performances are validated on more challenging scenarios and compared to an existing method, whose authors made their datasets public. Consequently, the results achieved highlight the modularity of the BN. In addition, our proposed algorithm outperforms the existing method, since it provides more accurate ego-lane localization: 85.35% compared to 77%.

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“…One of the major research topics in the domain of autonomous navigation, is enabling vehicles to cope with any environment traffic condition while making the appropriate decision and guaranteeing the safety of maneuvers even in presence of uncertainty [1], [2]. Although multiple Advanced Driver Assistance Systems (ADAS) have successfully improved safety [3], fatal car crashes still occur [4], [5].…”

Section: Introduction a Motivationmentioning

confidence: 99%

Reliable Risk Management for Autonomous Vehicles based on Sequential Bayesian Decision Networks and Dynamic Inter-Vehicular Assessment

Iberraken¹,

Adouane²,

Denis³

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Guaranteeing the safety of an autonomous vehicle (AV) is a challenging task, especially if the perceived environment is highly uncertain and other road users deviate from their expected trajectories. In this paper, we propose a probabilistic overall strategy for risk assessment and management of AV in highway through a Sequential Level Bayesian Decision Network (SLBDN) and an appropriate analytical formalization of criteria for anomaly detection based on a Dynamic Predicted Inter-Distance Profile (DPIDP) between vehicles. Accordingly, the proposed system is designed to take the suitable maneuver decision, have a safety retrospection and verification over the current maneuver risk and take appropriate evasive action autonomously from moving obstacles. Moreover, this probabilistic framework accounts for measurements uncertainty through an Extended Kalman Filter (EKF) and for vehicles' maximum capacities. Since the proposed strategy has a short response time, integrating safety verification in the decision-making process makes real time evasive decisions possible. Several simulation results show the good performance of the overall proposed control architecture, mainly in terms of efficiency to handle probabilistic decision-making even for risky scenarios.

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Multi-Level Bayesian Decision-Making for Safe and Flexible Autonomous Navigation in Highway Environment

Cited by 26 publications

References 10 publications

End-to-End Probabilistic Ego-Vehicle Localization Framework

End-to-End Probabilistic Ego-Vehicle Localization Framework

Probabilistic framework for ego-lane determination

Reliable Risk Management for Autonomous Vehicles based on Sequential Bayesian Decision Networks and Dynamic Inter-Vehicular Assessment

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