Learning Switching Models for Abnormality Detection for Autonomous Driving

By Mohamad Baydoun, Damian Campo, Valentina Sanguineti, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni. 2018

43 citations recorded by the source corpus.

We present an approach to learn a model to estimate the dynamical states at continuous and discrete inference levels when trajectory information is available. We learn from sparse data a probabilistic switching model that generates trajectories associated with a stationary plan of an agent. The learned generative model is used within a Markov Jump Linear System (MJLSs) to switch among set of space dependent linear filters that analyze new trajectories and detect deviations from the learned model based on internal innovation measurements. We show examples of application of the proposed approach to learn filters for evaluating deviations from a reference human driving task execution that includes static and dynamic obstacle avoidance.

Subfields: Artificial Intelligence, Automotive Engineering, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohamad Baydoun
2. Giulia Slavic
3. Lucio Marcenaro
4. Damian Campo
5. Carlo S. Regazzoni

Citations in the corpus

• Recent Advances of Continual Learning in Computer Vision: An Overview (2021, neutral): “More precisely, they proposed to use the lowdimension latent features in between the encoder and the decoder of a Variational Autoencoders (VAE), which can further be integrated with other sensory information using a Markov Jump Particle Filter [131].”
• Continual Learning Of Predictive Models In Video Sequences Via Variational Autoencoders (2020, methodology): “The MJPF uses a Particle Filter coupled with a bank of Kalman Filters (KFs) to infer continuous and discrete level information.”