Map-matching is widely used in automotive navigation systems to locate vehicle positions on a given digital road map. Various map-matching algorithms have been developed focusing on different application needs. Within the Ghosthunter project, a weighting-function-based map-matching algorithm has been developed for detecting wrong-way driving in order to improve road safety, particularly in Autobahn entrance and exit areas. This paper aims at exploring the potential use of lane-level attributes and height data in improving the success rate of the previously presented algorithm. This algorithm performs well in entrance and exit areas to the Autobahn, with a high success rate of 99.5% in identifying the road on which the vehicle is actually travelling. In the enhanced algorithm presented in this paper the weight coefficients used for computing the total weighting score of candidate roads are adjusted with the aid of one or both kinds of these precise data. The results confirmed that the usage of these precise data can effectively help to detect and correct mismatches at junctions and overpasses.

This article proposes a batch-mode algorithm to handle the large databases generated from experimentations using probe vehicles. This algorithm can locate raw Global Positioning System (GPS) positions on a map, but can also be used to correct map-matching errors introduced by real time map-matching algorithms. For each journey, the algorithm globally searches for the closest path to the GPS positions, and so is inspired from the “path to path” algorithm's family. It uses the Multiple Hypothesis Technique (MHT) and relies on an innovative weighting system based on the area between the GPS points and the arcs making up the path. For high performance, the algorithm uses an iterative program and the data is stored in tree form.

Vehicular communication technologies are becoming staples of modern societies. This paper proposes a new positioning algorithm for vehicular networks. The algorithm is a non-classic Multi-Dimensional Scaling Filter (MDSF) that builds on a novel and computationally effective Multi-Dimensional Scaling (MDS) solution covariance estimation technique and also a Maximum Likelihood (ML) filter. In general a major drawback of the non-classic MDS is the high computational cost because of its iterative nature. It is shown that a special blend between vehicular Map-Matching (MM) and MDSF considerably reduces the number of iterations and the convergence time, making the MDSF a suitable algorithm for vehicular network positioning. The performance of MDSF is compared with that of an Extended Kalman Filter (EKF) together with the Cramar Rao Lower Bound (CRLB). It is shown through simulation that for all types of traffic conditions MDSF performs better than EKF and closer to CRLB than EKF. It is also shown that both MDSF and EKF algorithms are robust to typical Global Positioning System (GPS) outages in deep urban canyons. CRLB also proves that Cooperative Positioning (CP) in general has the ability to bridge short GPS outages.

Application of map-matching techniques to GPS positions can provide accurate vehicle location information in challenging situations. The Hidden Markov Model (HMM) is a statistical model that is well known for providing solutions to temporal recognition applications such as text and speech recognition. This paper introduces a novel map-matching algorithm based on HMM for GPS-based wheelchair navigation. Given GPS positions, a hidden Markov chain model is established by using both geometric data and the topology of sidewalk segments. The map-matching algorithm employs the Viterbi algorithm to estimate correct sidewalk segments as hidden states in a HMM in order to match GPS trajectory on the corresponding segment sequence. The HMM-based map-matching algorithm was validated on a campus sidewalk network for wheelchair navigation. The results show an improvement in tracking a wheelchair in dense urban conditions both in accuracy and in computational time.

This paper presents development and application of a real-time virtual differential map-matching approach which makes use of the slow drifting property of the GPS errors and the continuous and gradual evolving characteristic of map errors to improve the accuracy and computational efficiency. A differential vector is created to approximate the real-time deviation, which is corrected continuously along with the vehicle movement during the map-matching process. Real-life application of the algorithm to the City of Hefei, a metropolis of China, shows that it corrects both GPS errors and digital map errors reasonably well with improved computational efficiency.

The acquisition of accurate and timely traffic information is a vital precondition to rational traffic decision making. Intelligent Transportation Systems (ITS) are bound to be the outcome when modern traffic systems develop to a high degree. In ITS, instantaneous traffic information can be collected by the Floating Car Data (FCD) method. Based on the establishment of the Shenzhen Urban Transportation Simulation System (SUTSS) in China, the authors explored how to use 4000 taxis as the data collection sensors in Shenzhen, a southern city in China which borders Hong Kong. The authors introduce the procedures and algorithms for the computation and map-matching of road segment velocities to a digital road network. To superimpose the near real-time traffic information onto a digital map, coordinate transformation is required and the transformation precision is analyzed using field testing data. Due to the nature of FCD, continuous GPS data such as routing velocities and coordinates can be collected by any GPS equipped vehicle. Therefore, relevant algorithms are developed and utilized for the map-matching according to probability and statistical theories. To evaluate the reliability of proposed map-matching method, the confidence levels are calculated statistically, from which it can be determined whether the positioning data is valid or not with predefined threshold values. Furthermore, road segment velocity matching methods based on the Metropolis criteria is extended and relevant validation is carried out through the comparison of estimated and measured results. The major objective of this method is to obtain more accurate road segment travel time through the combination of those estimated by FCD and historical ones. This can significantly improve the reliability of instantaneous traffic information before its web publication. The final part of this paper introduces the architecture and the realization of a web Geographical Information System (GIS) and FCD-based instantaneous traffic information dissemination system for the whole of Shenzhen City.

This paper presents a novel method for localization of mobile robots in structured environments. The estimation of the position and orientation of the robot relies on the minimisation of the partial Hausdorff distance between ladar range measurements and a floor plan image of the building. The approach is employed in combination with an extended Kalman filter to obtain accurate estimates of the robot's position, heading and velocity. Good estimates of these variables were obtained during tests performed using a differential drive robot, thus demonstrating that the approach provides an accurate, reliable and computationally feasible alternative for indoor robot localization and autonomous navigation.