Automatic Generation Method of Test Scenario for ADAS Based on Complexity

Xia, Qing; Duan, Jianli; Gao, Feng; Chen, Tao; Cai, Yang

doi:10.4271/2017-01-1992

Cited by 32 publications

(16 citation statements)

References 17 publications

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“…The main issue deriving from the use of such models is related to their training. As a matter of fact, a large and well-structured dataset, with all possible scenarios, is required to achieve good training quality [75,76]. In this context, the DL and RL, which can assure a progressive CAV learning of the scenario, are well-suited, efficient and good performing solutions for future systems.…”

Section: Predictionmentioning

confidence: 99%

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

et al. 2021

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Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.

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Section: Predictionmentioning

confidence: 99%

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

et al. 2021

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“…Improved combined testing algorithms were used to reduce the number of scenarios to be tested and increase the complexity of scenarios. Xia et al [27] used an improved PICT method to generate a more compact LDW system test suite, which ensured complete coverage of the specified t-way combination. Tuncali et al [28] used a combination of two-way coverage arrays to generate discrete parameter combinations of test scenarios and presented automatic falsification methods to identify challenging scenarios in the perception system of automated vehicles.…”

Section: A Parameterization Of Test Scenarios By the Combinatorial Testing Strategymentioning

confidence: 99%

Test Scenarios Construction Based on Combinatorial Testing Strategy for Automated Vehicles

Shu

Liu

et al. 2021

IEEE Access

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Scenario-based testing is an important verification and certification measure to ensure the safety of automated vehicles. In view of the existing test scenario composition methods, which may miss some critical scenario problems that have low occurrence probability, we fully combined the ego-vehicle with the possible relative positions and movement directions of surrounding traffic participants based on a complex scenario group. We applied scenario-screening rules to obtain the functional test scenarios with different traffic environments and driving task complexities, which ensured the coverage of the test scenarios and reduced the number of test scenarios. The problem arose that the amount of test cases was too large after the discretized combination of test scenario parameters, so we adopted a three-way combinatorial testing strategy to greatly reduce the number of test cases. Taking the complicated lane changing scenario of the egovehicle as an example, the simulation method was adopted, and the critical test cases were obtained by screening through safety indicators. Finally, the K-medoids algorithm clustering method was used to further reduce the number of critical test cases, and a pairwise combinatorial test strategy was used to combine dynamic scenarios and static scenario elements to obtain critical test cases for closed-road testing. INDEX TERMSAutomated vehicles; combinatorial testing strategy; functional test scenarios; critical test cases; safety indicators

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“…Importance based sampling [50] usually contains three major steps. First, it needs to analyze the scene elements, clarify the scene elements, and discretize the continuous scene elements.…”

Section: Importance Based Samplingmentioning

confidence: 99%

A Scenario-Based Development Framework for Autonomous Driving

2020

Preprint

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This article summarizes the research progress of scenario-based testing and development technology for autonomous vehicles. We systematically analyzed previous research works and proposed the definition of scenario, the elements of the scenario ontology, the data source of the scenario, the processing method of the scenario data, and scenario-based V-Model. Moreover, we summarized the automated test scenario construction method by random scenario generation and dangerous scenario generation.

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Automatic Generation Method of Test Scenario for ADAS Based on Complexity

Cited by 32 publications

References 17 publications

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Test Scenarios Construction Based on Combinatorial Testing Strategy for Automated Vehicles

A Scenario-Based Development Framework for Autonomous Driving

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