Real-Time Fully Unsupervised Domain Adaptation for Lane Detection in Autonomous Driving

While deep neural networks are being utilized heavily for autonomous driving, they need to be adapted to new unseen environmental conditions for which they were not trained. We focus on a safety critical application of lane detection, and propose a lightweight, fully unsupervised, real-time adaptation approach that only adapts the batch-normalization parameters of the model. We demonstrate that our technique can perform inference, followed by on-device adaptation, under a tight constraint of 30 FPS on Nvidia Jetson Orin. It shows similar accuracy (avg. of 92.19%) as a state-of-the-art semi-supervised adaptation algorithm but which does not support real-time adaptation.Comment: Accepted in 2023 Design, Automation & Test in Europe Conference (DATE 2023) - Late Breaking Result

Waveguide Invariant Active Sonar Target Detection and Depth Classification in Shallow Water

<p>Reverberation and clutter are two of the principle obstacles to active sonar target detection in shallow water. Diffuse seabed backscatter can obscure low energy target returns, while clutter discretes, specific features of the sea floor, produce temporally compact returns which may be mistaken for targets of interest. Detecting weak targets in the presence of reverberation and discriminating water column targets from bottom clutter are thus critical to good performance in active sonar. Both problems are addressed in this thesis using the time-frequency interference pattern described by a constant known as the waveguide invariant which summarizes in a scalar parameter the dispersive properties of the ocean environment. </p><p>Conventional active sonar detection involves constant false alarm rate (CFAR) normalization of the reverberation return which does not account for the frequency-selective fading in a wideband pulse caused by multipath propagation. An alternative to conventional reverberation estimation is presented, motivated by striations observed in time-frequency analysis of active sonar data. A mathematical model for these reverberation striations is derived using waveguide invariant theory. This model is then used to motivate waveguide invariant reverberation estimation which involves averaging the time-frequency spectrum along these striations. An evaluation of this reverberation estimate using real Mediterranean data is given and its use in a generalized likelihood ratio test (GLRT) based CFAR detector is demonstrated. CFAR detection using waveguide invariant reverberation estimates is shown to outperform conventional cell-averaged and frequency-invariant CFAR detection methods in shallow water environments producing strong reverberation returns which exhibit the described striations. Results are presented on simulated and real Mediterranean data from the SCARAB98 experiment. </p><p>The ability to discriminate between water column targets and clutter discretes is vital to maintaining low false alarm rates in active sonar. Moreover, because of the non-stationarity of the active sonar return, classification is most typically achieved using a single snapshot of test data. As an aid to classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide-invariant spectral density matrix (WI-SDM) which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is performed by a waveguide-invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) and averaging the signal replica vectors over the unknown channel parameters. Simulation and real data results from the SCARAB98, CLUTTER07, and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach.</p>Dissertatio

AUV active perception: Exploiting the water column

Autonomous Underwater Vehicles (AUVs) present a low-cost alternative or supplement to existing underwater surveillance networks. The NATO STO Centre for Maritime Research and Experimentation is developing collaborative autonomous behaviours to improve the performance of multi-static networks of AUVs. In this work we lay the foundation to combine a range-dependent acoustic model with a three dimensional measurement model for a linear array within a Bayesian framework. The resulting algorithm is able to provide the vehicles with an estimation of the target depth together with the more usual information based on a planar assumption (i.e. target latitude and longitude). Results are shown through simulations and as obtained from the REP16 sea trial where for the first time a preliminary implementation of the method was deployed in the C-OEX vehicles

Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. We also provide extensive simulation results to demonstrate the validity of the proposed approach