Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of
ground transportation. It is anticipated that ordinary vehicles will one day be replaced with …

Real-time, accurate, and robust localisation is critical for autonomous vehicles (AVs) to
achieve safe, efficient driving, whilst real-time performance is essential for AVs to achieve …

Probabilistic techniques (such as Extended Kalman Filter and Particle Filter) have long been
used to solve robotic localization and mapping problem. Despite their good performance in …

This paper introduces a new interval constraint propagation (ICP) approach dealing with the
real‐time vehicle localization problem. Bayesian methods like extended Kalman filter (EKF) …

The vehicle's localization is classically achieved by Bayesian methods like Extended
Kalman Filtering. Such methods provide an estimated position with its associated …

Interval constraint propagation (ICP) algorithms allow to solve problems described as
constraint satisfaction problems (CSP). ICP has been successfully applied to vehicle …

Vehicle ego-localization is commonly achieved by Bayesian methods like Extended Kalman
Filtering. New approaches based on interval analysis intend to achieve the same goal in a …

This paper provides an approach for controlling the level of risk when operating highly
automated transportation systems like cars, trains and similar. Such systems replace human …

Vehicle localization can be stated as a Constraint Satisfaction Problem (CSP). Interval
Constraint Propagation (ICP) algorithms allow us to solve CSPs. This paper discusses the …

Vehicle localization is a part of many automotive applications where reliability is of crucial
importance. Probabilistic techniques such as Kalman and Particle filtering have long been …