Fault Diagnosis of Self-Localization in Autonomous Vehicles Using a Model-Based Approach

Abstract

Autonomous driving is a development that has gained a lot of attention lately, because it can lead to major improvements in the mobility sector. One example of a research project that aims to develop vehicles that are capable of reaching the highest level of autonomy in driving, is the WEpods project. The goal of this research is in line with this aim, having the thesis objective defined as follows: let the WEpods continue driving in autonomous mode more often than is currently the case.

The WEpod shuttles are not yet completely able to drive autonomously due to their inability to handle unexpected behavior (terminology: faults). Currently, such faults need to be detected and solved by a steward, who will manually initiate a safe stop if necessary. The localization module, which is responsible for localizing the vehicle on a map, sometimes generates unreliable location estimates. This poses two challenges. First, the fact that there is a mismatch between reality and the sensor outcomes of the localization module that needs to be detected. Second, the question of how to prevent the system from showing behavior that is different from what is desired (terminology: failure) in case such a fault is present (terminology: fault tolerant contol). Fault tolerant control can be performed in either a passive or an active manner. The passive approach ensures that either the faults are prevented or the system is able to mitigate them by anticipation in the design. The approach evolves from passive to active fault tolerant control when an on-line adaptation of the system control is made. For applications in autonomous driving, it is apparent that it is important to handle not only anticipated faults, but also to be able to deal with unexpected faults in an on-line manner. This on-line fault tolerant control approach involves two fault diagnosis steps that lead to solving the first challenge: detection and isolation.

A so-called model-based fault diagnosis approach turned out to be most suitable, as it has been used for similar applications in the past. However, a model-based fault diagnosis approach has not yet been implemented for detecting and isolating faults in a localization module of autonomous driving, indicating the scientific relevance of this research. In the model-based approach, kinematic and dynamic equations of the research vehicle (WEpod) are used to build a computational model. This model is then subjected to an observer, that is able to compare the model outcomes with the actual measurements in an off-line way. A residual is drawn up by taking the difference between the model outcomes and the measurements. A threshold is computed based on noise on the measurements to compare the residual with. When the residual exceeds the threshold, an alarm is raised. This way, the system itself has been enabled to detect faults when they occur internally.

Inclusion of the suggested fault diagnosis approach in an on-line manner into the system is a big step towards fully autonomous driving of the WEpods, and therefore the goal of this research is met.