Limitations of positioning systems for developing digital maps and locating vehicles according to the specifications of future driver assistance systems

Jiménez, Felipe; Naranjo, José Eugenio; Fernández, Fernando García; Zato, José G.; Armingol, José María; Escalera, Arturo de la; Aparicio, Francisco

doi:10.1049/iet-its.2010.0042

Cited by 15 publications

(11 citation statements)

References 13 publications

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“…However, due to multiple factors, including positioning systems errors [18], the behavior of the drivers, and the imperfections of the road, this knowledge was not trivial. To obtain the lane through which each vehicle moves, the algorithm uses the data previously stored with the road mapping (Section 2.1).…”

Section: Estimation Of the Driving Lanementioning

confidence: 99%

Impact on Congestion and Fuel Consumption of a Cooperative Adaptive Cruise Control System with Lane-Level Position Estimation

et al. 2018

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Abstract:In recent years, vehicular communications systems have evolved and allowed for the improvement of adaptive cruise control (ACC) systems to make them cooperative (cooperative adaptive cruise control, CACC). Conventional ACC systems use sensors on the ego-vehicle, such as radar or computer vision, to generate their behavioral decisions. However, by having vehicle-to-X (V2X) onboard communications, the need to incorporate perception in the vehicle is drastically reduced. Thus, in this paper a CACC solution is proposed that only uses communications to make its decisions with the help of previous road mapping. At the same time, a method to develop these maps is presented, combining the information of a computer vision system to correct the positions obtained from the navigation system. In addition, the cut-in and cut-out maneuvers for a CACC platoon are taken into account, showing the tests of these situations in real environments with instrumented vehicles. To show the potential of the system in a larger-scale implementation, simulations of the behavior are provided under dense traffic conditions where the positive impact on the reduction of traffic congestion and fuel consumption is appreciated.

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Section: Estimation Of the Driving Lanementioning

confidence: 99%

Impact on Congestion and Fuel Consumption of a Cooperative Adaptive Cruise Control System with Lane-Level Position Estimation

et al. 2018

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“…Wetzlar, Germany) and an RMS FES 33 gyroscopic platform (RMS Dipl.-Ing Schäfer GmbH & Co, KG. Hamburg, Germany) [70][71][72][73]. The slopes profile is shown in Figure 4.…”

Section: Results On a Real Road Sectionmentioning

confidence: 99%

System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information

Jiménez

Cabrera-Montiel

2014

Energies

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Nowadays, reducing the energy and fuel consumption of road vehicles is a key issue. Different strategies have been proposed. One of them is to promote Eco-driving behaviour among drivers. Most Eco-driving tips take into account only the road stretch where the vehicle is located. However, larger improvements could be achieved if information from subsequent stretches is used. The main objective of this work is to develop a system to warn the driver in real time of the optimal speed that should be maintained on every road segment in order to optimize the energy used and the fuel consumed while observing a time schedule. The system takes into account the road vertical profile, the fixed and variable speed limits and the traffic information retrieved using V2V and V2I communications. The system has been tested on real road sections with satisfactory results in fuel savings.

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“…Inertial guidance can also be used in conjunction with GPS to improve the GPS accuracy [10] but can also deal with short-time GPS failures [11]. Experiments show that the error of vehicle positioning using INS is about 3.2±1.1% of the distance traveled [12], which also shows that independent INS can only be used for short-range positioning. For a UAV, positioning methods based on image registration have significant advantages because of the following reasons: 1) Top View: Images obtained by the UAV have a consistent top view perspective with the satellite.…”

Section: Introductionmentioning

confidence: 99%

Radar Image-Based Positioning for USV Under GPS Denial Environment

Smart

Ahmed

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-Unmanned Surface Vehicle (USV) is an important application of unmanned systems and these USVs provide safe and secure operation in hostile environments. But these USVs are highly reliant on their positioning system such as Global Position System (GPS) and loss of positioning information from GPS can cause catastrophe. To overcome this positioning challenge for a USV under GPS denial environment, we propose a real-time positioning algorithm based on radar and satellite images to determine the USV position. The algorithm takes coastline as a registration feature to implement an image registration between a horizontal viewing angle image from a radar and a vertical viewing angle image from a satellite. The contributions of this paper consist of two parts. Firstly, a coastline feature extraction method based on edge gray features for both radar and satellite images is provided. Secondly, a high efficiency image registration method which takes the dimensionality reduction distance as an indicator was proposed for USV embedded system. The results from six typical application scenarios show that the maximum positioning error of the proposed algorithm is 28.02 m under the worst case. A continuous positioning experiment shows that the average error of the algorithm is 9.77m, which indicates that the algorithm can meet the positioning requirements of a USV under GPS denial environment.

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Limitations of positioning systems for developing digital maps and locating vehicles according to the specifications of future driver assistance systems

Cited by 15 publications

References 13 publications

Impact on Congestion and Fuel Consumption of a Cooperative Adaptive Cruise Control System with Lane-Level Position Estimation

Impact on Congestion and Fuel Consumption of a Cooperative Adaptive Cruise Control System with Lane-Level Position Estimation

System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information

Radar Image-Based Positioning for USV Under GPS Denial Environment

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