Wu et al., 2023 - Google Patents
Risk Assessment and Enhancement Suggestions for Automated Driving Systems through Examining Testing Collision and Disengagement ReportsWu et al., 2023
View PDF- Document ID
- 11168995188893081793
- Author
- Wu K
- Wu W
- Liao C
- Lin W
- Publication year
- Publication venue
- Journal of Advanced Transportation
External Links
Snippet
The California Department of Motor Vehicles (DMV) reports, including disengagement and collision reports, provide information on each accident or disengagement activity for on‐road testing of autonomous driving systems (ADSs) and autonomous vehicles (AVs) …
- 238000004458 analytical method 0 abstract description 13
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/01—Customer relationship, e.g. warranty
- G06Q30/018—Business or product certification or verification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance, e.g. risk analysis or pensions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Boggs et al. | Exploring the who, what, when, where, and why of automated vehicle disengagements | |
| Wang et al. | Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches | |
| Sohrabi et al. | Quantifying the automated vehicle safety performance: A scoping review of the literature, evaluation of methods, and directions for future research | |
| Wang et al. | Exploring causes and effects of automated vehicle disengagement using statistical modeling and classification tree based on field test data | |
| Liu et al. | Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology | |
| Alambeigi et al. | Crash themes in automated vehicles: A topic modeling analysis of the California Department of Motor Vehicles automated vehicle crash database | |
| Gietelink et al. | Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations | |
| Wood et al. | The potential regulatory challenges of increasingly autonomous motor vehicles | |
| Hyun et al. | Assessing crash risk considering vehicle interactions with trucks using point detector data | |
| Yarlagadda et al. | Assessing safety critical driving patterns of heavy passenger vehicle drivers using instrumented vehicle data–An unsupervised approach | |
| So et al. | Development and validation of a vehicle dynamics integrated traffic simulation environment assessing surrogate safety | |
| Wu et al. | Risk Assessment and Enhancement Suggestions for Automated Driving Systems through Examining Testing Collision and Disengagement Reports | |
| Klück et al. | An empirical comparison of combinatorial testing and search-based testing in the context of automated and autonomous driving systems | |
| Rustad | Products Liability for Software Defects in Driverless Cars | |
| Zhang et al. | Safety evaluation method in multi-logical scenarios for automated vehicles based on naturalistic driving trajectory | |
| CN110325410A (en) | Data analysis set-up and program | |
| Ding et al. | Exploratory analysis of injury severity under different levels of driving automation (SAE Level 2-5) using multi-source data | |
| Adanu et al. | An analysis of the effects of crash factors and precrash actions on side impact crashes at unsignalized intersections | |
| Singh et al. | Conflict-Based safety evaluations at unsignalized intersections using surrogate safety measures | |
| Joni et al. | Analysis of traffic accident severity in Baghdad city using Binary Logistic Regression Model | |
| Zhou et al. | Evaluating Autonomous Vehicle Safety Performance Through Analysis of Pre-Crash Trajectories of Powered Two-Wheelers | |
| Liao et al. | Hierarchical quantitative analysis to evaluate unsafe driving behaviour from massive trajectory data | |
| Yan et al. | A comparison of patterns and contributing factors of ADAS and ADS involved crashes | |
| Wu et al. | Reliability and safety assessment of automated driving systems: review and preview | |
| Moomen et al. | An analysis of factors influencing driver action on downgrade crashes using the mixed logit analysis |