Accurate lateral positioning from map data and road marking detection

Gruyer, Dominique; Belaroussi, Rachid; Revilloud, Marc

doi:10.1016/j.eswa.2015.08.015

Cited by 60 publications

(41 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Then, a particle filter was employed to update the position and heading of the vehicle with the detected road markings and GPS-IMU output. A road marking detection based localization technique using; two cameras directed towards the ground, GPS-IMU dead reckoning, odometry, and a precise marker location map was proposed in [116]. Another vision based method with a single camera and geo-referenced traffic signs was presented in [117].…”

Section: A Priori Map-based Localizationmentioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

View full text Add to dashboard Cite

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS

show abstract

Section: A Priori Map-based Localizationmentioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Digital maps can help with advanced driver-assistance systems and autonomous driving. For example, driving applications such as positioning [1,2], driving path planning [3], and decision-making [4] benefit from the auxiliary information in digital maps. Digital maps are used to provide the surrounding information of a vehicle, which facilitates perception applications [5,6] for intelligent driving systems.…”

Section: Introductionmentioning

confidence: 99%

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

Zheng

Song²,

et al. 2019

IJGI

View full text Add to dashboard Cite

With the development of autonomous driving, lane-level maps have attracted significant attention. Since the lane-level road network is an important part of the lane-level map, the efficient, low-cost, and automatic generation of lane-level road networks has become increasingly important. We propose a new method here that generates lane-level road networks using only position information based on an autonomous vehicle and the existing lane-level road networks from the existing road-level professionally surveyed without lane details. This method uses the parallel relationship between the centerline of a lane and the centerline of the corresponding segment. Since the direct point-by-point computation is huge, we propose a method based on a trajectory-similarity-join pruning strategy (TSJ-PS). This method uses a filter-and-verify search framework. First, it performs quick segmentation based on the minimum distance and then uses the similarity of two trajectories to prune the trajectory similarity join. Next, it calculates the centerline trajectory for lanes using the simulation transformation model by the unpruned trajectory points. Finally, we demonstrate the efficiency of the algorithm and generate a lane-level road network via experiments on a real road.

show abstract

“…Research approaches in the literature have focused on tightly coupling vision sensors and enhancing the feature detection algorithm such as in Gruyer, Belaroussi, and Revilloud (), Hara and Saito (), Levinson and Thrun (), Pink (), and Wolcott and Eustice (). In this paper, the lane detection camera is an off‐the‐shelf system.…”

Section: Introductionmentioning

confidence: 99%

“…The detection of the closest markings usually leads to less complex and more robust detection as argued in Gruyer et al. (). We focus on improving the GNSS/DR accuracy by modeling the measurement errors.…”

Section: Introductionmentioning

confidence: 99%

Road‐Centered Map‐Aided Localization for Driverless Cars Using Single‐Frequency GNSS Receivers

Tao

Bonnifait

Frémont

et al. 2017

Journal of Field Robotics

View full text Add to dashboard Cite

Accurate localization with high availability is a key requirement for autonomous vehicles. It remains a major challenge when using automotive sensors such as single‐frequency Global Navigation Satellite System (GNSS) receivers, a lane detection camera, and proprioceptive sensors. This paper describes a method that enables the estimation of stand‐alone single‐frequency GNSS errors by integrating the measurements from a forward‐looking camera matched with lane markings stored in a digital map. It includes a parameter identification method for a shaping model, which is evaluated using experimental data. An algebraic observability study is then conducted to prove that the proposed state vector is fully observable in a road‐oriented frame. This observability property is the basis to develop a road‐centered Extended Kalman filter (EKF) that can maintain the observability of every component of the state vector on any road, whatever its orientation. To accomplish this, the filter needs to handle road changes, which it does using bijective transformations. The filter was implemented and tested intensely on an experimental vehicle for driverless valet parking services. Field results have shown that the performance of the estimation process is better than solutions based on EKF implemented in a fixed working frame. The proposed filter guarantees that the drift along the road direction remains bounded. This is very important when the vehicle navigates autonomously. Furthermore, the road‐centered modeling improves the accuracy, consistency, and robustness of the localization solver.

show abstract

Accurate lateral positioning from map data and road marking detection

Cited by 60 publications

References 7 publications

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

Road‐Centered Map‐Aided Localization for Driverless Cars Using Single‐Frequency GNSS Receivers

Contact Info

Product

Resources

About