Scope

This workshop aims at the localization integrity problem of automated vehicles (e.g., SAE L3 and above). The concept of integrity is defined as “a measure of trust which can be placed in the correctness of the information supplied by the total system”. To guarantee the safe driving of an AV in varying environments, measures of the localization information gathered from different sensors, such as LiDAR, IMU, GNSS, are required. Continuously and reliably estimating a vehicle’s position in varying driving environments is essential for autonomous driving and safe operation. However, dynamic and complex traffic environments make high-integrity localization very challenging in the vehicular domain.

In our 1st iLoc workshop, we want to identify potential solutions to remedy the problems, such as  uncertainties  in  both  environmental  perception  and  vehicle  localization,  vision-based  deep  learning models  for  integrity  monitoring,  and  the  development  of  standardization  of  integrity  localization  for automated vehicles. 

Topics of Interest

At this workshop, the research topics of interests include but are not limited to:

• What are the leading factors for high-integrity localization for AVs?
• What are the integrity measures for AVs?
• What are the developments of standardization in the vehicular domain for integrity performance?
• How to estimate the uncertainty and integrity risks in e.g., conventional and deep learning-based models for localization and autonomous driving?
• How to combine a vehicle kinematic model and road geometry to improve integrity estimation?
• Uncertainty propagation and updates while an AV drives in different environments.
• Map reference with its own integrity measure.
• Quantification and representation of the models’ aleatoric and epistemic uncertainties
• Uncertainty estimation of LiDAR point clouds registration and imagery data processing Kinematics of AV
• State-of-the-art deep learning multi-modal data fusion