Modeling L1-GPS Errors for an Enhanced Data Fusion with Lane Marking Maps for Road Automated Vehicles - Archive ouverte HAL Access content directly
Conference Papers Year : 2015

Modeling L1-GPS Errors for an Enhanced Data Fusion with Lane Marking Maps for Road Automated Vehicles


This paper describes a method which models the time correlation errors of a standalone L1-GPS receiver by integrating front-view camera measurements map-matched with a lane marking map. An identification method of the parameters of the shaping model is presented and evaluated with real data. The observability of the augmented state vector is demonstrated according to an algebraic definition. A positioning solver based on extended Kalman filtering with measured input is developed in the local ENU (East-North-Up) frame. This formulation allows including efficiently speed sensors in a dead-reckoned way. Outdoor experiments results carried out with a ground truth system are used to illustrate the performance of the proposed method. 1 INTRODUCTION Estimating the pose in real-time is a primary function for road automated vehicles. Recently, highly accurate digital maps have become key components for autonomous vehicle navigation and can enhance the positioning performance [1][2][3]. In this paper, we propose to use a lane marking map of the navigation area with centimetre-level precision. The map consists of two-lane roadways with dashed lane markings in the centre of the road and solid lane markings on the both sides of the road. Lane marking is expressed by polylines in a local frame near the navigation area. In order to exploit this lane marking map, we have integrated a camera from the Lane Departing Warning Systems of the experimental vehicle. The camera's lane marking detection module is able to measure the lateral distance between the detected lane marking and the vehicle in the camera frame. The camera can also identify the type of the detected lane markings (solid, dashed and road edge). Since the camera gives relative measurements, the next step is to relate the measures with the vehicle's state. For this, we have proposed a refined camera observation model which express camera measurement by a function of the state vector and the parameters of the detected lane marking [4]. So a data association process is necessary to associate a camera measurement with a certain lane marking segment in the map database. It is similar to a map matching process. The difference is that the map matching process can introduce a non-zero mean errors by simply projecting the vehicle position onto a segment which usually represents the road centreline. However, the camera has limitations. For example, there is no lane markings in some parts of the navigation area and the camera often fails to detect lane marking at crossroads. If the uncertainty domain grows too quickly during these times, it can be very challenging to distinguish different lanes when the camera retrieves measurements. Therefore Dead-Reckoning (DR) sensors are integrated to increase the performance particularly in terms of availability. We demonstrate the observability of the gyro bias thanks to the camera observation. It is important to note that the camera observation can only improve the positioning in lateral direction for a given moment, although the longitudinal accuracy is also improved if the road changes direction. GPS is very useful to compensate DR's long-term drift and it is mandatory for a cold start initialization of the localization system. However the L1-GPS positioning errors are not white and can be affected by strong biases, which are mainly due to atmosphere propagation delays, multi-path and real-time orbit errors, particularly in urban area. In this paper, we study GPS biases shaping models that are used in a filter by augmenting the state vector. The GPS errors are studied thanks to the use of a high-end localization system with centimetre-level accuracy. Autocorrelations of different sequences are analysed to find repeatable behaviour supposing that the bias is stationary (GPS measures with abrupt bias changing according to multi-path is supposed to be rejected by the localization solver). The bias is modeled by an autoregressive model. Parameters identification methods are discussed for integrating the shaping filter into the localization solver. The observability of GPS biases being not obvious, one of our contribution is the demonstration of the observability of the augmented state vector, which contains the pose of the vehicle, a gyro bias and GPS errors, according to an algebraic definition. The remainder is organized as follows. Section 2 introduces the GPS error modeling. Section 3 describes the system modeling and demonstrates the observability of the state vector. Outdoor experimental results are reported in Section 4. Section 5 concludes the paper.
Fichier principal
Vignette du fichier
FP_ENC-093.pdf (576.22 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01142021 , version 1 (14-04-2015)


  • HAL Id : hal-01142021 , version 1


Zui Tao, Philippe Bonnifait. Modeling L1-GPS Errors for an Enhanced Data Fusion with Lane Marking Maps for Road Automated Vehicles. European Navigation Conference (ENC 2015), Apr 2015, Bordeaux, France. ⟨hal-01142021⟩
317 View
269 Download


Gmail Facebook Twitter LinkedIn More