Dr. rer. nat.

Cheng, Chih-Hong

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Now showing 1 - 4 of 4
  • Publication
    Sensing and Machine Learning for Automotive Perception: A Review
    ( 2023)
    Pandharipande, Ashish
    ;
    Cheng, Chih-Hong
    ;
    Dauwels, Justin
    ;
    Gurbuz, Sevgi Z.
    ;
    Ibanez-Guzman, Javier
    ;
    Li, Guofa
    ;
    Piazzoni, Andrea
    ;
    Wang, Pu
    ;
    Santra, Avik
    Automotive perception involves understanding the external driving environment as well as the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This paper provides an overview of different sensor modalities like cameras, radars, and LiDARs used commonly for perception, along with the associated data processing techniques. Critical aspects in perception are considered, like architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined.
  • Publication
    Are Transformers More Robust? Towards Exact Robustness Verification for Transformers
    ( 2023)
    Liao, Brian Hsuan-Cheng
    ;
    Cheng, Chih-Hong
    ;
    Esen, Hasan
    ;
    Knoll, Alois
    As an emerging type of Neural Networks (NNs), Transformers are used in many domains ranging from Natural Language Processing to Autonomous Driving. In this paper, we study the robustness problem of Transformers, a key characteristic as low robustness may cause safety concerns. Specifically, we focus on Sparsemax-based Transformers and reduce the finding of their maximum robustness to a Mixed Integer Quadratically Constrained Programming (MIQCP) problem. We also design two pre-processing heuristics that can be embedded in the MIQCP encoding and substantially accelerate its solving. We then conduct experiments using the application of Land Departure Warning to compare the robustness of Sparsemax-based Transformers against that of the more conventional Multi-Layer-Perceptron (MLP) NNs. To our surprise, Transformers are not necessarily more robust, leading to profound considerations in selecting appropriate NN architectures for safety-critical domain applications.
  • Publication
    Formally Compensating Performance Limitations for Imprecise 2D Object Detection
    ( 2022-08-25)
    Schuster, Tobias
    ;
    Seferis, Emmanouil
    ;
    Burton, Simon
    ;
    Cheng, Chih-Hong
    In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations related to the misalignment of bounding-box predictions to the ground truth: the prediction bounding box cannot be perfectly aligned with the ground truth. We formally prove the minimum required bounding box enlargement factor to cover the ground truth. We then demonstrate that this factor can be mathematically adjusted to a smaller value, provided that the motion planner uses a fixed-length buffer in making its decisions. Finally, observing the difference between an empirically measured enlargement factor and our formally derived worst-case enlargement factor offers an interesting connection between quantitative evidence (demonstrated by statistics) and qualitative evidence (demonstrated by worst-case analysis) when arguing safety-relevant properties of machine learning functions.
  • Publication
    Logically Sound Arguments for the Effectiveness of ML Safety Measures
    ( 2022)
    Cheng, Chih-Hong
    ;
    Schuster, Tobias
    ;
    Burton, Simon
    We investigate the issues of achieving sufficient rigor in the arguments for the safety of machine learning functions. By considering the known weaknesses of DNN-based 2D bounding box detection algorithms, we sharpen the metric of imprecise pedestrian localization by associating it with the safety goal. The sharpening leads to introducing a conservative post-processor after the standard non-max-suppression as a counter-measure. We then propose a semi-formal assurance case for arguing the effectiveness of the post-processor, which is further translated into formal proof obligations for demonstrating the soundness of the arguments. Applying theorem proving not only discovers the need to introduce missing claims and mathematical concepts but also reveals the limitation of Dempster-Shafer’s rules used in semi-formal argumentation.