Can low-cost road vehicles positioning systems fulfil accuracy specifications of new ADAS applications?

Some new Advanced Driver Assistance Sy stems (ADAS) need on-the-lane vehicle positioning on accurate digital maps, but present applications of vehicle positioning do not justify the surcharge of very ac curate equipment such as DGPS or high-cost inertial systems. For this reason, performance of GPS in autonomous mode is analyzed. Although satisfactory results can be found, in some areas GPS signal is lost or degraded, so it is necessary to know the positioning error when using only inertial system data. A th eoretical approach based on the uncertainty propagation law is used to esti mate the upper limit of distance that can be travelled fulfilling the specifications of an assistance system. Tests results support the conclusions of this approach. Finally, the comb ination of GPS and inertial systems is studied, resulting that the theoretical approach is valid when inertial measurements are used right from the start of GPS signal de gradation, without waiting for a complete loss.The work reported in this paper has been partly funded by the Spanish Ministry of Science and Innovation (SIAC project TRA2007-67786-C02-01 and TRA2007-67786-C02-02) and the CAM project SEGVAUTO.Publicad

IMM-Based lane-change prediction in highways with low-cost GPS/INS

The prediction of lane changes has been proven to be useful for collision avoidance support in road vehicles. This paper proposes an interactive multiple model (IMM)-based method for predicting lane changes in highways. The sensor unit consists of a set of low-cost Global Positioning System/inertial measurement unit (GPS/IMU) sensors and an odometry captor for collecting velocity measurements. Extended Kalman filters (EKFs) running in parallel and integrated by an IMM-based algorithm provide positioning and maneuver predictions to the user. The maneuver states Change Lane (CL) and Keep Lane (KL) are defined by two models that describe different dynamics. Different model sets have been studied to meet the needs of the IMM-based algorithm. Real trials in highway scenarios show the capability of the system to predict lane changes in straight and curved road stretches with very short latency times.Ministerio de Fomento: FOM/2454/200

Multisensor Localization Architecture for High-Accuracy and High-Integrity Land-based Applications

Emerging safety-related applications like autonomous vehicles will require high levels of navigation performance in terms of accuracy while still satisfying stringent requirements of integrity, availability and continuity. Achieving sub-meter accuracy in land-based scenarios with GNSS-based solutions can only be achieved with carrier-phase based approaches in combination with additional sensors. So that these solutions can be considered for future certified systems, the safety aspect must be ensured, which is still a challenge in the presence of local GNSS threats (like multipath or NLOS) as well as in multisensor architectures. In this work, we propose a multisensor architecture that uses float Real-Time Kinematic (RTK) GNSS with additional integrity information from an augmentation network. The architecture also considers different layers of protection against local GNSS threats that supports the rigorous design of integrity monitoring algorithms and protection level computation. GNSS is combined with additional sensors like Inertial Measurement Unit (IMU) and a robust relative location of the vehicle is complemented with stereo camera and vision processing. This allows for different possible types of localization modes. The algorithms are validated with real measurements collected with a car during a measurement in Rome, Italy. The results show clearly that our design can achieve high accuracy while ensuring high integrity

A Hybrid Intelligent Multisensor Positioning Methodology for Reliable Vehicle Navigation

With the rapid development of intelligent transportation systems worldwide, it becomes more important to realize accurate and reliable vehicle positioning in various environments whether GPS is available or not. This paper proposes a hybrid intelligent multisensor positioning methodology fusing the information from low-cost sensors including GPS, MEMS-based strapdown inertial navigation system (SINS) and electronic compass, and velocity constraint, which can achieve a significant performance improvement over the integration scheme only including GPS and MEMS-based SINS. First, the filter model of SINS aided by multiple sensors is presented in detail and then an improved Kalman filter with sequential measurement-update processing is developed to realize the filtering fusion. Further, a least square support vector machine- (LS SVM-) based intelligent module is designed and augmented with the improved KF to constitute the hybrid positioning system. In case of GPS outages, the LS SVM-based intelligent module trained recently is used to predict the position error to achieve more accurate positioning performance. Finally, the proposed hybrid positioning method is evaluated and compared with traditional methods through real field test data. The experimental results validate the feasibility and effectiveness of the proposed method

High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS

Awaiting permission to publish abstrac

On the Recursive Joint Position and Attitude Determination in Multi-Antenna GNSS Platforms

Global Navigation Satellite Systems’ (GNSS) carrier phase observations are fundamental in the provision of precise navigation for modern applications in intelligent transport systems. Differential precise positioning requires the use of a base station nearby the vehicle location, while attitude determination requires the vehicle to be equipped with a setup of multiple GNSS antennas. In the GNSS context, positioning and attitude determination have been traditionally tackled in a separate manner, thus losing valuable correlated information, and for the latter only in batch form. The main goal of this contribution is to shed some light on the recursive joint estimation of position and attitude in multi-antenna GNSS platforms. We propose a new formulation for the joint positioning and attitude (JPA) determination using quaternion rotations. A Bayesian recursive formulation for JPA is proposed, for which we derive a Kalman filter-like solution. To support the discussion and assess the performance of the new JPA, the proposed methodology is compared to standard approaches with actual data collected from a dynamic scenario under the influence of severe multipath effects