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The advent of pervasive autonomous systems such as self-driving cars and drones has raised questions about their safety and trustworthiness. This is particularly relevant in the event of on-board subsystem errors or failures. In this research, we show how encoded Extended Kalman Filter can be used to detect anomalous behaviors of critical components of nonlinear autonomous systems: sensors, actuators, state estimation algorithms and control software. As opposed to prior work that is limited to linear systems or requires the use of cumbersome machine learned checks with fixed detection thresholds, the proposed approach necessitates the use of time-varying checks with dynamically adaptive thresholds. The method is lightweight in comparison to existing methods (does not rely on machine learning paradigms) and achieves high coverage as well as low detection latency of errors. A quadcopter and an automotive steer-by-wire system are used as test vehicles for the research and simulation and hardware results indicate the overhead, coverage and error detection latency benefits of the proposed approach. 

In this paper we propose a framework for concurrent detection of soft computation errors in particle filters which are finding increasing use in robotics applications. The particle filter works by sampling the multi-variate probability distribution of the states of a system (samples called particles, each particle representing a vector of states) and projecting these into the future using appropriate nonlinear mappings. We propose the addition of a `check' state to the system as a linear combination of the system states for error detection. The check state produces an error signal corresponding to each particle, whose statistics are tracked across a sliding time window. Shifts in the error statistics across all particles are used to detect soft computation errors as well as anomalous sensor measurements. Simulation studies indicate that errors in particle filter computations can be detected with high coverage and low latency.