Due to the fact that the Hydraulic Hybrid Vehicle (HHV) which has a double-function, energy management and stability control, is a great advantage in the HHV, it is necessary to research the coordinated control between regenerative braking and stability. The research of the coordinated control between regenerative braking and stability of HHV is at the forefront of the study in this aspect. Based on the above factors, in this study, we propose an algorithm of regenerative braking and stability coordinated control. Firstly, we put forward the assembly institutions of the Parallel Hydraulic Hybrid Vehicle (PHHV), then we adopt the logic threshold algorithm to realize the distribution control and use the incremental joint PI control with variable parameters to realize the vehicle electronic stability control system (ESC) control. Simulink/Simscape in the MATLAB software is utilized for the offline simulation of the control algorithm of PHHV. The result of the simulation shows the effectiveness of the regenerative braking force distribution and ESC algorithm.
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.
The launching of autonomous driving vehicle relies on enormous tests and a reasonable test evaluation system. Compared with the real vehicle test characterized by long test cycle, high cost, high risk and low coverage, virtual simulation tests are found with the advantages of infinite expansion of scenarios, arbitrary setting of traffic flow, dynamics-based parameterized vehicle simulation model and customized configuration of driver model. This paper based on international standards of L1-L2 automatic driving functions related to real world testing, delivered a methodology for test cases design. through function-based evaluation indicators and scenario-based evaluation indicators, a systematic and logical simulation test evaluation method and framework based on driving scenario is proposed.
KEYWORDS: Monte Carlo methods, Roads, Autonomous driving, Scene classification, Scene simulation, Autonomous vehicles, Statistical analysis, Unmanned vehicles, Standards development, Reflection
In order to better conform to the real driving scene, intelligent driving simulation test needs to generalize the test scene on the basis of the collected natural driving data. In this context, a scenario generalization method for intelligent driving simulation based on Latin hypercube sampling is proposed in this paper, which makes up for the defect of the traditional Monte Carlo scenario generalization, in which the scene parameters are not covered enough in each dimension and ensures the coverage of the generalized scene. The comparison experiment shows that the generalization method of intelligent driving simulation scenarios based on Latin hypercube sampling can be applied in a limited time. Almost all scenarios are covered within the sampling times. Under the condition that the sampling times are the same, the risk degree of simulated scenarios can be guaranteed to be consistent with the real data sources and have higher robustness.
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.
The test methods of autonomous driving are mainly divided into SIL, MIL, VIL, real vehicle test, etc.; the scenario-based virtual simulation verification method has huge technical advantages in test coverage, test repeatability, test safety, etc., and is an effective method for future autonomous driving function test verification. With the improvement of technology and demand, autonomous driving developers have gradually increased their investment in simulation testing. How to evaluate the comparison between simulation test results and actual test authenticity is a major challenge for autonomous driving. The high-definition map software used in this paper can realize intelligent lane fitting and fast connection, and realize fast and smooth connection in advance while ensuring the consistency between the edited road network and the actual environment. By collecting real scene maps, a high-definition map conforming to the Open Drive format that can be used in real vehicles and simulations is generated, which solves the problem of common use of lane-level high-definition maps in intelligent driving vehicle testing and simulation testing.
Scenario definition for Automated Driving Function Validation is the state-of-the-art research area for autonomous driving, unification of the scenario description methods and language is the crucial step for the scenario definition. Based on the simulation scenario database established by China Automotive Technology and Research Center (CATARC) using natural language processing methods, to connect the human readable scenario definition with programmable scenario coding, provide the bridge between scenario definition with simulation testing, for better unify between scenario definition and virtual scenario building. With the participation of many enterprises, the corresponding test of automatic driving has become an access condition for the implementation of the corresponding technologies of automatic driving. Enterprises and institutions all over the world are trying to establish a unified scenario database to achieve the wide sharing and unification.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.