TY  - GEN
N2  - While autonomy offers a solution to many issues facing the maritime and naval industry, assessing the safety and reliability of autonomous shipping is one of the key challenges. Established methods and standards for conventional ships are still relevant but do not account for the challenges that come with the application of Artificial Intelligence (AI) and cyber-physical systems for Maritime Autonomous Surface Ships (MASS). Such systems require specialised safety assessment techniques. Scenario-based safety assessment for autonomous systems is one of the potential approaches and represents the state of the art. In this research, we conduct a feasibility study on identification and classification of seafaring scenarios, as described in COLREG, from Automatic Identification System (AIS) data. Furthermore, the statistical distributions for the parameters of these scenarios are empirically determined. We illustrate the utility of our methods using real-life AIS data from the Strait of Dover. Results indicate that AIS data can be a rich data source for identifying real-life scenarios that can be used for safety assessment of MASS.
AB  - While autonomy offers a solution to many issues facing the maritime and naval industry, assessing the safety and reliability of autonomous shipping is one of the key challenges. Established methods and standards for conventional ships are still relevant but do not account for the challenges that come with the application of Artificial Intelligence (AI) and cyber-physical systems for Maritime Autonomous Surface Ships (MASS). Such systems require specialised safety assessment techniques. Scenario-based safety assessment for autonomous systems is one of the potential approaches and represents the state of the art. In this research, we conduct a feasibility study on identification and classification of seafaring scenarios, as described in COLREG, from Automatic Identification System (AIS) data. Furthermore, the statistical distributions for the parameters of these scenarios are empirically determined. We illustrate the utility of our methods using real-life AIS data from the Strait of Dover. Results indicate that AIS data can be a rich data source for identifying real-life scenarios that can be used for safety assessment of MASS.
AD  - TNO, Monitoring and Control Services, Groningen, The Netherlands;
AD  - TNO, Integrated Vehicle Safety, Helmond, The Netherlands
T1  - Scenario Identification for Safety Assessment of Autonomous Shipping using AIS Data
DA  - 2020-10-05
AU  - Snijders, R
AU  - Elrofai, H
L1  - https://library.imarest.org/record/7696/files/INEC_2020_Paper_90.pdf
JF  - Conference Proceedings of INEC
VL  - INEC 2020
PY  - 2020-10-05
ID  - 7696
L4  - https://library.imarest.org/record/7696/files/INEC_2020_Paper_90.pdf
KW  - Autonomous Shipping
KW  - Automatic Identification System
KW  - Scenario Identification and Classification
KW  - Scenario Based Safety Assessment
TI  - Scenario Identification for Safety Assessment of Autonomous Shipping using AIS Data
Y1  - 2020-10-05
L2  - https://library.imarest.org/record/7696/files/INEC_2020_Paper_90.pdf
LK  - https://www.imarest.org/events/inec-2020
LK  - https://library.imarest.org/record/7696/files/INEC_2020_Paper_90.pdf
UR  - https://www.imarest.org/events/inec-2020
UR  - https://library.imarest.org/record/7696/files/INEC_2020_Paper_90.pdf
ER  -