Abstract

Autonomous and wirelessly connected vehicles face various challenges before effective commercial deployment. Key among these challenges is accurate and reliable awareness of world interactions. Awareness arises from onboard sensors and from ubiquitous communication between vehicles and infrastructure. Vehicle coordination and safety specifications necessitate reliable “where-in-lane” (i.e., decimeter accuracy) knowledge of vehicle position relative to other vehicles and the environment. This presentation will address vehicle state estimation with a focus on high precision and reliability.

Sensor fusion is critical to achieving these application requirements. Several of the sensors (e.g., vision, radar, Lidar, ultrasound, Global Navigation Satellite Systems (GNSS)) have various spurious measurement types. Standard Extended Kalman Filter (EKF) approaches are not sufficiently reliable at removing the effects of such spurious measurements because the EKF approach must decide at the time each measurement arrives whether it is valid. If deemed as valid, the measurement is used and discarded; otherwise it is not used and discarded. When that decision is wrong, either measurement information is lost or the state and covariance estimates are corrupted. Either situation can result in divergence of the EKF.

An alternative is to maintain all recent measurement data within a moving time-horizon. This window of data can be processed within a Bayesian framework to extract the optimal state trajectory estimate over the time-horizon. Because the time window of data is maintained it is straightforward to change the assumptions as to which data are valid and reprocess the data, allowing consideration of multiple fault scenario assumptions. Therefore, this approach is referred to as a Contemplative Real-Time (CRT) estimator. It is closely related to Moving Horizon Estimation (MHE) and Simultaneous Localization and Mapping (SLAM). This presentation will review the interrelationships between the EKF, Iterated Extended Kalman Filter (IEKF), and CRT within the Bayesian framework; discuss fault accommodation; present comparative experimental results; and discuss recent results on computationally efficient carrier phase integer ambiguity resolution over time windows.

Short bio

Jay A. Farrell is a Professor and Chair of the Department of Electrical and Computer Engineering at the University of California, Riverside. He earned B.S. degrees in physics and electrical engineering from Iowa State University, and M.S. and Ph.D. degrees in electrical engineering from the University of Notre Dame. At Charles Stark Draper Lab (1989-1994), he received the Engineering Vice President's Best Technical Publication Award in 1990, and Recognition Awards for Outstanding Performance and Achievement in 1991 and 1993. He has served the IEEE Control Systems Society (CSS) as Finance Chair for three IEEE CDC`s (`95, `01, and `03), on the Board of Governors for two terms (`03-`06, `12-`14), as Vice President Finance and Vice President of Technical Activities,  as General Chair of IEEE CDC 2012, and as President in 2014. He was named a GNSS Leader to Watch for 2009-2010 by GPS World Magazine in May 2009 and a winner of the Connected Vehicle Technology Challenge by the U.S. Department of Transportation`s (DOT`s) Research and Innovative Technology Administration in July 2011. He is author of over 250 technical publications, and three books, a Distinguished Member of IEEE CSS, a Fellow of AAAS, and a Fellow of the IEEE.