Probabilistic framework for ego-lane determination

Kasmi, Abderrahim; Denis, Dieumet; Aufrère, Romuald; Chapuis, Roland

doi:10.1109/ivs.2019.8813843

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…In addition, the probabilistic HMM calculation and formalization were not explicitly defined, leading to a non-intuitive definition for the emission and transition probabilities. Kasmi et al [ 130 ] proposed an improvement of the latter work by defining a formalization of the HMM that avoids empirical definitions and thus leads to a better understanding of the system. Furthermore, they use the knowledge of surrounding vehicles to better infer the correct number of lanes.…”

Section: Lane-level Localization (Lll)mentioning

confidence: 99%

A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios

Laconte

Kasmi

Aufrère

et al. 2021

Sensors

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In the context of autonomous vehicles on highways, one of the first and most important tasks is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take into account the information from several sensors and fuse them with data coming from road maps. The localization problem on highways can be distilled into three main components. The first one consists of inferring on which road the vehicle is currently traveling. Indeed, Global Navigation Satellite Systems are not precise enough to deduce this information by themselves, and thus a filtering step is needed. The second component consists of estimating the vehicle’s position in its lane. Finally, the third and last one aims at assessing on which lane the vehicle is currently driving. These two last components are mandatory for safe driving as actions such as overtaking a vehicle require precise information about the current localization of the vehicle. In this survey, we introduce a taxonomy of the localization methods for autonomous vehicles in highway scenarios. We present each main component of the localization process, and discuss the advantages and drawbacks of the associated state-of-the-art methods.

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Section: Lane-level Localization (Lll)mentioning

confidence: 99%

A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios

Laconte

Kasmi

Aufrère

et al. 2021

Sensors

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“…More high level features systems fuse visual features from the road scene: surrounding vehicles [17], lanes marking [18] [19], road clues such as arrows marking [20], lane marking colors [21], lane marking and adjacent vehicles [22]. These visual cues are fed into a fusion framework: Bayesian filter (BF) [17], Bayesian Network (BN) [20] [18] [21], Hidden Markov Model (HMM) [19] or combination of BN and HMM [22]. A worth mentioning work is the one presented in [23] in which a lane-level accurate map is used in order to match the correct localization of the ego-vehicle.…”

Section: Model-driven Approachesmentioning

confidence: 99%

“…Once the ego-lane localization is estimated, we have to perform the lane-level localization to correctly choose the right lane on which the vehicle travels. To do so, we proposed in [22] a probabilistic framework that is split into three stages. Thus, the presented LLL algorithm is an extension of our work proposed in [22].…”

Section: Lane Level Localization (Lll)mentioning

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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“…They used GNSS measure and the number of lanes of the road, retrieved from a map service OpenStreetMap prior to enhancement of the visual ego-lane index estimation [24]. Similarly, Kasmi et al [25] also used the lane information from Open-StreetMap to assist ego-lane determination and introduced a modular Bayesian network (BN) to infer the ego-lane from multiple inaccurate information sources. Svensson et al [26] proposed a Bayesian filter to infer probability for the correct lane assignment by fusing lane-marking and vehicle detection data and map information.…”

Section: Introductionmentioning

confidence: 99%

Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection

Zhang

et al. 2021

Sensors

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Correct ego-lane index estimation is essential for lane change and decision making for intelligent vehicles, especially in global navigation satellite system (GNSS)-challenged environments. To achieve this, we propose an ego-lane index estimation approach in an urban scenario based on particle filter (PF). The particles are initialized and propagated by dead reckoning with inertial measurement unit (IMU) and odometry. A lane-level map is used to navigate the particles taking advantage of topologic and geometric information of lanes. GNSS single-point positioning (SPP) can provide position estimation with meter-level accuracy in urban environments, which can limit drift introduced by dead reckoning for updating the weight of each particle. Light detection and ranging (LiDAR) is a common sensor in an intelligent vehicle. A LiDAR-based road boundary detection method provides distance measurements from the vehicle to the left/right road boundaries, which provides a measurement for importance weighting. However, the high precision of the LiDAR measurements may put a tight constraint on the distribution of particles, which can lead to particle degeneration with sparse particle sets. To deal with this problem, we propose a novel step that shifts particles laterally based on LiDAR measurements instead of importance weighting in the traditional PF scheme. We tested our methods on an urban expressway at a low traffic volume period to ensure road boundaries can be detected by LiDAR measurements at most time steps. Experimental results prove that our improved PF scheme can correctly estimate ego-lane index at all time steps, while the traditional PF scheme produces wrong estimations at some time steps.

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Probabilistic framework for ego-lane determination

Cited by 11 publications

References 15 publications

A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios

A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios

End-to-End Probabilistic Ego-Vehicle Localization Framework

Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection

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