Joblift
Location
Nantes | France
Job description
TITLE
Safe and Reliable Multisensor Navigation in Urban Environments: a Case Study for Soft Mobility
PROBLEMATIC AND CONTEXT
With the continuous development of intelligent objects, new mobility applications, from pedestrian navigation aids to autonomous vehicles, require highly accurate positioning information. Especially for the reliability-critical applications, such as guidance for blind people, “Pay-as-you-Drive” insurance, providing accurate positioning information is essential. With the mobility transition in Europe towards low carbon emission, an increasing number of urban soft mobility has emerged, from e-scooters, e-bikes to self-balancing scooters or even self-driving bicycles.
However, the users of soft mobility are vulnerable compared to other transport users, where accidents can be easily fatal once happen. Their safety can be improved by location-based services providing highly accurate and reliable positioning information, which is also the goal of positioning integrity monitoring. Positioning integrity monitoring algorithms [1] aim to detect and exclude measurement anomalies and to estimate an uncertainty level for the positioning information provided by the system according to the users" specifications.
The objective of this internship is to study and improve a multisensory (GPS/INS/Magnetometer/barometer/odometer) positioning algorithm for soft mobility, particularly bikes and e-scooters by adding positioning integrity control modules, to enhance the safety of the soft mobility.
The internship will be done based on the multisensory positioning device (ULISS - Ubiquitous Localization with Inertial Sensors and Satellites) and the algorithms designed by the GEOLOC laboratory. Two parallel main Work Packages (WP) are included in the internship.
WP1: High accurate multisensory fusion positioning algorithm for bikes and e-scooters
1. Understand and master the existing Artificial Intelligence (AI) and Extended Kalman Filter (EKF) algorithm LIGHT-PDR [2] and its codes which is designed for
3. Add an anomaly detection module to detect and remove the faulty measurements;
4. Add a positioning uncertainty estimation module either by using traditional statistical methods or AI algorithm;
5. Evaluate the performance
WP2: Minimum Operational Performance (MOP) Requirement Definition for Positioning Soft Mobility
The objective of this WP is to define the necessary parameters for the positioning integrity monitoring modules which need to be added in WP1.
1. Criticality analysis will be done to identify the safety-critical scenarios/events for soft mobility users. For each critical scenario, the user needs as well as the criteria for MOP will be defined.
2. By searching in the current literature or existing transport regulations for the soft mobility, the following positioning requirements (including but not limited to) need to be defined in a consolidated way: Positioning Accuracy [m], System availability (%), Continuity and integrity risk, Time-to-Alert [s], Alert Limit (AL). More details about the definitions of these parameters can be found in [3].
3. Propose a methodology for defining these MOP for soft mobility positioning systems to facilitate the generalization in different countries.
[1] Zhu, N., Betaille, D., Marais, J. and Berbineau, M., 2020. GNSS integrity monitoring schemes for terrestrial applications in harsh signal environments. IEEE Intelligent Transportation Systems Magazine, 12(3), pp.81-91.
[2] Li, Ziyou, Ni Zhu, and Valérie Renaudin. "LIGHT-PDR: Light Indoor GNSS Carrier Phase Positioning with Machine Learning and Inertial Signal Fusion for Pedestrian Navigation." 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2023.
[3] Zhu, Ni, Juliette Marais, David Bétaille, and Marion Berbineau. "GNSS position integrity in urban environments: A review of literature." IEEE Transactions on Intelligent Transportation Systems 19, no. 9 (2018): 2762-2778.
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Job tags
Salary
600 €
