| Recurring |
one_organization |
(a) The software failure incident related to Tesla's self-driving system mistaking a horse-drawn carriage for a large semi-truck is not the first time such incidents have occurred with Tesla's technology. The article mentions previous incidents where Tesla vehicles using full self-driving software rolled through stop signs without halting completely, leading to a recall of nearly 54,000 cars and SUVs [131017]. Additionally, in another incident, a Tesla in full self-driving mode allegedly ran over a child-size mannequin during a test, failing to detect the stationary dummy's presence in the road [131017].
(b) The article does not provide specific information about similar incidents happening at other organizations or with their products and services. |
| Phase (Design/Operation) |
design, operation |
(a) The software failure incident in the article can be attributed to the design phase. The incident occurred due to the Tesla self-driving system's inability to accurately identify and classify a horse-drawn carriage on the highway, mistaking it for various other objects like a truck, pedestrian, and sedan. This confusion highlights a flaw in the system's design and the input provided by engineers, as the software did not account for such unconventional scenarios [131017].
(b) Additionally, the article mentions concerns raised by safety advocates regarding Tesla's full self-driving software malfunctioning and posing risks to other motorists and pedestrians. This aspect relates to the operation phase, where the failure is linked to factors introduced by the operation or misuse of the system, such as testing the vehicles on public roads with untrained drivers and the software's inability to detect obstacles like a child-size mannequin [131017]. |
| Boundary (Internal/External) |
within_system |
(a) within_system: The software failure incident involving Tesla's self-driving system mistaking a horse-drawn carriage for a large semi-truck occurred due to factors originating from within the system. The incident was a result of the AI-powered software's inability to accurately identify and classify the unexpected scenario of a horse-drawn carriage on the highway. The system's computer vision technology, which includes cameras, ultrasonic sensors, and radar, failed to properly interpret the environment, leading to confusion in identifying the object in front of the vehicle [131017].
(b) outside_system: The software failure incident involving Tesla's self-driving system mistaking a horse-drawn carriage for a large semi-truck was influenced by factors originating from outside the system. The incident occurred due to the presence of an unexpected scenario on the highway, namely the horse-drawn carriage, which the software was not adequately programmed to recognize. The system's struggle to adapt to this external factor highlights the challenges software faces when encountering novel or unconventional situations that fall outside the scope of its programming [131017]. |
| Nature (Human/Non-human) |
non-human_actions |
(a) The software failure incident in the article occurred due to non-human actions, specifically the Tesla's self-driving system going haywire when encountering a horse-drawn carriage on the highway. The AI-powered software became confused and displayed incorrect visualizations on the dashboard, mistaking the carriage for various other objects like a truck, pedestrian, and sedan [131017].
(b) The article does not mention any contributing factors introduced by human actions that led to the software failure incident. |
| Dimension (Hardware/Software) |
hardware, software |
(a) The software failure incident occurring due to hardware:
- The article reports on a Tesla self-driving system going haywire when the vehicle encountered a horse-drawn carriage on the highway, confusing it with a large semi-truck. This confusion was due to the limitations in the computer vision system and sensors used by Tesla's self-driving technology, which failed to accurately identify the horse and buggy on the road [131017].
(b) The software failure incident occurring due to software:
- The software failure incident in the article is primarily attributed to the limitations and inaccuracies in the software used in Tesla's self-driving technology. The system's software, which includes computer vision algorithms and sensor processing, was unable to correctly identify and classify the unexpected scenario of a horse-drawn carriage on the highway, leading to confusion and misinterpretation of the surroundings by the AI-powered software [131017]. |
| Objective (Malicious/Non-malicious) |
non-malicious |
(a) The software failure incident reported in Article 131017 is categorized as non-malicious. The incident involved Tesla's self-driving system encountering difficulties when driving behind a horse-drawn carriage on the highway, mistaking it for a large semi-truck. The confusion and misinterpretation by the AI-powered software were not intentional but rather a result of the system struggling to adapt to unexpected scenarios [131017]. The incident highlights the challenges faced by even sophisticated software in handling unique and unconventional situations on the road. |
| Intent (Poor/Accidental Decisions) |
poor_decisions |
(a) The software failure incident involving Tesla's self-driving system mistaking a horse-drawn carriage for a large semi-truck on the highway can be attributed to poor decisions. The incident highlights how the software struggled to adapt to unexpected scenarios, such as encountering a horse and buggy, which was not accounted for in the system's programming [131017]. This failure can be seen as a result of poor decisions made during the development and testing phases of the self-driving technology, leading to inaccuracies and potentially dangerous situations on the road. |
| Capability (Incompetence/Accidental) |
development_incompetence, accidental |
(a) The software failure incident in the article can be attributed to development incompetence. The incident where Tesla's self-driving system went haywire behind a horse-drawn carriage on the highway showcases a failure in the software's ability to accurately identify and classify objects on the road. The system's confusion between a horse-drawn carriage and a large semi-truck demonstrates a lack of professional competence in programming the software to handle unexpected scenarios [131017].
(b) Additionally, the incident can also be considered as accidental. The confusion and misclassification of the horse-drawn carriage by Tesla's self-driving software can be seen as an accidental outcome of the system's limitations in adapting to unconventional objects on the road. The incident was shared on TikTok, where users found humor in the software's confusion, indicating that the failure was not intentional but rather an unintended consequence of the software's programming [131017]. |
| Duration |
temporary |
The software failure incident reported in Article 131017 regarding Tesla's self-driving system encountering issues with a horse-drawn carriage on the highway can be categorized as a temporary failure. The incident was a result of the software struggling to adapt to an unexpected scenario, specifically mistaking the carriage for various other objects like a truck, pedestrian, and sedan. This confusion was not a permanent issue but rather a temporary failure caused by the unique circumstances of encountering a horse-drawn carriage, which was not a typical object the software had been trained to recognize [131017]. |
| Behaviour |
crash, omission, value, other |
(a) crash: The software failure incident in the article can be categorized as a crash. The Tesla's self-driving system went haywire when it encountered a horse-drawn carriage on the highway, leading to confusion in identifying the object correctly. The system displayed erratic behavior by continuously changing its identification from a truck to a pedestrian to a sedan and back to a big rig, ultimately failing to maintain its intended function of accurately recognizing the objects in its environment [131017].
(b) omission: The incident also involved an omission failure as the software omitted to correctly identify the horse-drawn carriage on the road. Despite using a computer vision system with cameras, ultrasonic sensors, and radar to perceive the environment, the system failed to include the horse and buggy in its recognition database, leading to the misidentification and confusion displayed in the visualization on the dashboard [131017].
(c) timing: There is no specific indication in the article that the software failure incident was related to timing issues where the system performed its intended functions too late or too early. The focus of the incident was primarily on the system's misidentification and confusion when encountering the horse-drawn carriage on the highway [131017].
(d) value: The software failure incident can also be attributed to a value failure as the system performed its intended functions incorrectly by misidentifying the objects on the road. The inaccurate identification of the horse-drawn carriage as a truck, pedestrian, and sedan showcased a failure in the system's ability to provide correct and valuable information to the driver, ultimately leading to confusion and potential safety concerns [131017].
(e) byzantine: The article does not provide evidence of the software failure incident exhibiting byzantine behavior, which involves erroneous and inconsistent responses and interactions within the system. The primary focus of the incident was on the misidentification and confusion displayed by the self-driving system when encountering the unexpected scenario of a horse-drawn carriage on the highway [131017].
(f) other: The other behavior exhibited by the software failure incident in the article could be described as unexpected or unpredictable behavior. The system's continuous changes in identification from a truck to a pedestrian to a sedan and back to a big rig, along with the shifting and sideways movement of the visualization on the dashboard, led to a surprising and unusual response from the software, causing amusement and confusion among the users witnessing the incident [131017]. |