As the core of autonomous vehicle technology, perception chips are essential for data processing and environmental sensing. The efficacy and reliability of these chips are strongly impacted by the various functional risk factors may confront in scenarios in the real world. This paper provide efficient preventive measures and an innovative method for addressing functional safety failures in perception chips across a variety of real-world scenarios. It systematically investigates typical failure scenarios in a variety of scenarios as well as offers a detailed explanation of the underlying difficulties. A range of novel functional safety testing techniques are introduced in the study, which builds on this basis and allows for quicker and more accurate fault processing. The study additionally explores recent developments and preventative methods to enhance the overall safety functionality of autonomous systems. The study conducts extensive simulation experiments and comparative analyses to show off the viability of the proposed approach, showcasing the notable improvements in terms of fault recognition rate and processing speed. Results from experiments support the method's significant advantages for enhancing the functionality in perception chips, leading to improved functional safety throughout the autonomous driving system.
KEYWORDS: System on a chip, Radar sensor technology, Radar signal processing, Radar, Autonomous driving, Detection and tracking algorithms, Automotive radar, Signal detection, Computing systems, Target detection
This paper presents an radar blockage recognition approach along with blindness prevention measures utilizing intelligent computing methods to address the challenges in radar chip-based autonomous driving systems. A process and algorithm are initially created for detection, enabling real-time and precise identification. The adaptive blockage performance controller and the automatic diagnosis and recovery strategy are then proposed as part of the adaptive blockage prevention strategy, which enables the system to quickly return to its normal operating state by intelligently adjusting parameters when the sensor blockage occurs. The conventional linear threshold methods are replaced with nonlinear machine learning to deal with complex performance variations, with the aim of lowering false positives and negatives through real-time optimization. A comparative experiment is conducted to show the effectiveness.
For analyzing risk behavior based on multi-source time series data, a fuzzy Mann-Kendall method for autonomous or ADAS system equipped vehicles is proposed. With the combination of Mann-Kendall and fuzzy rules, more accurate detection of the data breakpoints, outliers, and other features are achievable, and the whole statistical patterns are fully considerable. It can eliminate ambiguity and uncertainty in driving data with assessment efficacy, can provide interoperability and compatibility, can support several data distribution with trend modes, and can modify inference rules to adapt to different data sets and application scenarios. For validating the suggested approach purpose, 200 sets of cut-in data are abstracted. By comparing the result with the actual triggering sign, the result shows the method's viability.
A MHP-RGA (Multiple Hierarchical Projection Grey Relational Analysis) method is proposed for consistency and effectiveness assessment between validation results, which is focus on ADAS or autonomous driving functions under designed test case. The complex and interactive systemic output trace could be projected and calculated to the normalized relevance degree from multi-dimensional concerned levels such as the system architecture, component constitution, functional behavior, and measurement approaches. It can identify the inconsistencies sources between different validation types or test rounds (e.g simulation vs. open-road testing) with quantification. The experiment of FCW (front collision warning) scenario shows its abilities and the wide application prospect.
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