GPS and Inertial Systems for High Precision Positioning on Motorways

Naranjo, José Eugenio; Jiménez, Felipe; Aparicio, Francisco; Zato, José G.

doi:10.1017/s0373463308005249

Cited by 19 publications

(16 citation statements)

References 11 publications

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“…One possible solution is to use a detailed digital map to know any absolute position of the vehicle on it. However, this solution involves the availability of such maps with the required degree of precision and detail, which are not common [58], as well as a precise absolute positioning, which cannot be guaranteed in several areas [59]. Alternatively, the superelevation angle could be approximately obtained from the measurement of a gyroscopic platform [45].…”

Section: Road Superelevation Rate Influencementioning

confidence: 99%

Improving the Lane Reference Detection for Autonomous Road Vehicle Control

et al. 2016

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Autonomous road vehicles are increasingly becoming more important and there are several techniques and sensors that are being applied for vehicle control. This paper presents an alternative system for maintaining the position of autonomous vehicles without adding additional elements to the standard sensor architecture, by using a 3D laser scanner for continuously detecting a reference element in situations in which the GNSS receiver fails or provides accuracy below the required level. Considering that the guidance variables are more accurately estimated when dealing with reference points in front of and behind the vehicle, an algorithm based on vehicle dynamics mathematical model is proposed to extend the detected points in cases where the sensor is placed at the front of the vehicle. The algorithm has been tested when driving along a lane delimited by New Jersey barriers at both sides and the results show a correct behaviour. The system is capable of estimating the reference element behind the vehicle with sufficient accuracy when the laser scanner is placed at the front of it, so the robustness of the control input variables (lateral and angular errors) estimation is improved making it unnecessary to place the sensor on the vehicle roof or to introduce additional sensors.

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Section: Road Superelevation Rate Influencementioning

confidence: 99%

Improving the Lane Reference Detection for Autonomous Road Vehicle Control

et al. 2016

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“…These requirements are even greater when autonomous driving functions are introduced where, in many cases, knowledge of "where in the lane" is required [2]. However, conventional GNSS (Global Navigation Satellite System)-based positioning systems are not able to achieve these levels of accuracy under real traffic conditions, even when receivers that accept DGPS (Differential Global Positioning Systems) are available, since centimeter accuracy levels cannot be guaranteed along a whole route [3], much less so, in urban areas [4].…”

Section: Introductionmentioning

confidence: 99%

Accurate and Detailed Transversal Road Section Characteristics Extraction Using Laser Scanner

et al. 2018

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Road vehicle lateral positioning is a key aspect of many assistance applications and autonomous driving. However, conventional GNSS-based positioning systems and fusion with inertial systems are not able to achieve these levels of accuracy under real traffic conditions. Onboard perception systems provide knowledge of the surroundings of the vehicle, and some algorithms have been proposed to detect road boundaries and lane lines. This information could be used to locate the vehicle in the lane. However, most proposed algorithms are quite partial and do not take advantage of a complete knowledge of the road section. This paper proposes an integrated approach to the two tasks that provides a higher level of robustness of results: road boundaries detection and lane lines detection. Furthermore, the algorithm is not restricted to certain scenarios such as the detection of curbs; it could be also used in off-road tracks. The functions have been tested in real environments and their capabilities for autonomous driving have been verified. The algorithm is ready to be merged with digital map information; this development would improve results accuracy.

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“…Depending on the particular demands, higher or lower levels of accuracy are required when positioning the vehicle [ 2 ]. Thus, informative applications for navigation systems need not be especially accurate while other systems such as those focused on the improvement of traffic safety might need submetric precisions [ 3 ]. Generally speaking, the location of a vehicle is mainly accomplished by satellite positioning systems (e.g., GNSS).…”

Section: Introductionmentioning

confidence: 99%

“…However, these solutions are not limitless. In [ 3 ], the authors pointed out that the previous solutions accumulate a high amount of time without GPS coverage and the high precision levels offered by type 4 or type 5 positioning are available less than 37% of the time in some situations. On the other hand, the solution of using our own base stations for the differential correction has a very limited range and it is only acceptable for obtaining high levels of accuracy near the base station, so it is not an acceptable solution for real traffic conditions.…”

Section: Introductionmentioning

confidence: 99%

“…In all previous situations the signal reception losses are still occurring and so it is necessary to turn to methods that enable positioning even in those areas. To do so, inertial systems are usually employed, though these systems have a cumulative error [ 3 ] that makes their reliability decrease as the travelling distance increases [ 2 ]. In particular, [ 4 ] analyse the effect of using low-performance and low-cost sensors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Definition of an Enhanced Map-Matching Algorithm for Urban Environments with Poor GNSS Signal Quality

Jiménez¹,

Monzón²,

Naranjo³

2016

Sensors

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Vehicle positioning is a key factor for numerous information and assistance applications that are included in vehicles and for which satellite positioning is mainly used. However, this positioning process can result in errors and lead to measurement uncertainties. These errors come mainly from two sources: errors and simplifications of digital maps and errors in locating the vehicle. From that inaccurate data, the task of assigning the vehicle’s location to a link on the digital map at every instant is carried out by map-matching algorithms. These algorithms have been developed to fulfil that need and attempt to amend these errors to offer the user a suitable positioning. In this research; an algorithm is developed that attempts to solve the errors in positioning when the Global Navigation Satellite System (GNSS) signal reception is frequently lost. The algorithm has been tested with satisfactory results in a complex urban environment of narrow streets and tall buildings where errors and signal reception losses of the GPS receiver are frequent.

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GPS and Inertial Systems for High Precision Positioning on Motorways

Cited by 19 publications

References 11 publications

Improving the Lane Reference Detection for Autonomous Road Vehicle Control

Improving the Lane Reference Detection for Autonomous Road Vehicle Control

Accurate and Detailed Transversal Road Section Characteristics Extraction Using Laser Scanner

Definition of an Enhanced Map-Matching Algorithm for Urban Environments with Poor GNSS Signal Quality

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