You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
Additional Information Regarding EA22002
Investigation: EA22002

Date: 4/25/2024

EA22002 (upgraded from PE21020) was opened to conduct extensive crash analysis, human factors
analysis, and vehicle evaluations, and to assess vehicle control authority and driver engagement
technologies. After discussions with NHTSA, Tesla filed a Defect Information Report (Recall 23V838)
on December 12, 2023, applicable to all Tesla models produced and equipped with any version of
its Autopilot 1 system, which Tesla described as an SAE Level 2 (L2) Advanced Driver Assistance
System (ADAS). Tesla concluded in its 23V838 filing that, in certain circumstances, Autopilot’s
system controls may be insufficient for a driver assistance system that requires constant supervision
by a human driver. Tesla filed Recall 23V838 to address concerns regarding the Autopilot system
investigated in EA22002. These insufficient controls can lead to foreseeable driver disengagement
while driving and avoidable crashes.
ODI and other NHTSA subject matter experts performed substantial work in EA22002, including:

1

•

Sent multiple Information Request (IR) letters to Tesla and to L2 peer manufacturers to collect
the following information concerning L2 vehicle technology:
o Production and field data
o Engineering specifics concerning control authority, driver engagement, and operational
design domain
o Communications to customers
o Methods for detecting first responder scenes
o System changes, development, and validation/verification approaches
o Company approaches to crash evaluation
o Assessment of L2 system involvement with reported crashes

•

Conducted an in-depth crash analysis to characterize crash circumstances

•

Convened multiple technical meetings with Tesla to review specific crashes and system change
releases

•

Performed hands-on vehicle evaluations to assess vehicle human-machine interfaces and
evaluated human factors consideration in design and controls

•

Examined Tesla’s changes to Autopilot

•

Compared Autopilot to peer L2 systems in-field by:
o Control authority
o Operational Design Domain (ODD)
o Object and Event Detection and Response (OEDR)
o Driver engagement systems
o Ease of engagement

Autopilot refers to simultaneous engagement of TACC and Autosteer.

o

Naming convention

Crash Analysis
Throughout the PE21020 and EA22002 investigations, ODI observed a trend of avoidable crashes
involving hazards that would have been visible to an attentive driver. Before August 2023, ODI
reviewed 956 total crashes where Autopilot was initially alleged to have been in use at the time of, or
leading up to, those crashes. ODI’s crash review involved two separate actions:
1. A detailed analysis covered 446 crashes occurring from early 2018 through August 2022 that
relied on in-depth assessments of video from the subject vehicle onboard cameras, event data
recorders (EDR), data logs, and other information; and
2. A supplemental analysis, which focused on 510 Tesla incidents gathered through the Standing
General Order (SGO) process from August 2022 to the end of August 2023. ODI’s analysis of
these crashes relied primarily on Tesla’s narratives and videos and data logs obtained from Tesla
for a portion of the 510 crashes. When supporting videos and data logs were unavailable, ODI
used Tesla’s source material inventory as stated in the SGO narrative (such as full, partial or no
video, logs, etc.) to assign a confidence level to its assessment. The supplemental analysis drew
similar conclusions to those of the detailed analysis.
ODI continued to observe and follow up on SGO-reported crashes after August 2023 up to the recall
filing and generally observed conditions similar to those identified in the analyses.

Detailed Crash Analysis (Jan 2018 - Aug
2022)
Condition

Casualties

Crashes

Fatal
Crashes

Deaths

Injuries

Frontal Plane
Struck Vehicle
/ Object /
Person in
travel path

143

9

10

Yaw / Spin /
Understeer
(low traction
environment)

53

--

Inadvertent
Steering
Override
(Cancel AS,
keep TACC)

55

FSD-Beta
Crash

Supplemental Crash
Analysis (Aug 2022 Aug 2023)

Total

Crashes

Fatal
Crashes

Crashes

Fatal
Crashes

49

68

4

211

13

--

3

92

--

145

--

--

--

1

56

--

111

--

15

--

--

2

60

1

75

1

AnalysisExcludeIndeterminate

66

3

3

19

65

1

131

4

AnalysisExclude- Other
Vehicle Fault

83

--

--

19

82

4

165

4

AnalysisExcludeUnrelated to/ No full AP
engagement

31

3

5

8

50

4

81

7

Other

--

--

--

--

37

--

37

--

Total

446

15

18

101

510

14

956

29

Table 1: Detail and Supplemental Analysis Crash Counts, Conducted by NHTSA

Generally, the crashes that were the subject of ODI’s analysis fell into one of three categories:
Frontal Plane:
211 crashes were identified in which the frontal plane of the Tesla struck a vehicle or obstacle in its
path. This crash type includes the first responder crashes that prompted the original investigation.
When a driver is disengaged with the Tesla vehicle operating in Autopilot and the vehicle encounters
a circumstance outside of Autopilot’s object or event detection response capabilities (e.g., obstacle
detection and/or forward path planning), crash outcomes are often severe because neither the
system nor the driver reacts appropriately, resulting in high-speed differential and high energy crash
outcomes. The 211 crashes considered as part of this analysis resulted in 13 fatal crashes leading to
14 deaths and 49 injuries.
ODI’s analysis of crash data indicates that, prior to Recall 23V838, Autopilot’s design was not sufficient
to maintain drivers’ engagement. 109 of the 143 crashes from the detailed analysis included data
sufficient to measure the time between impact and the time a hazard would have come into the visual
field of an engaged driver. In more than half (59) of these crashes, the hazard was visible five or more
seconds prior to the impact, with a subset of 19 exhibiting a hazard visible for over 10 seconds prior
to the collision. For events unfolding faster, such as those where the hazard may have first been seen
less than two seconds prior to the crash, an attentive driver’s timely actions could have mitigated the
severity of a crash even if the driver may not have been able to avoid the crash altogether.
Hazard Visible Time Bin

Total

Percent

≥ 10 sec

19

17%

5 – 10 sec

40

37%

2 – 5 sec

42

39%

< 2 sec

8

7%

100%
Total
109
Table 2: Hazard Visible Time vs Roadway- Detailed Analysis, Conducted by NHTSA
For 135 incidents, the driver response to a hazard prior to impact was identified through a review of
the EDR and vehicle data logs. Drivers either did not brake or braked less than one second prior to the
crash in 82 percent of the incidents, and either did not steer or steered less than one second prior to
impact in 78 percent of the incidents.
For example, ODI reviewed a fatal crash that occurred in July 2023 in Warrenton, Virginia involving a
2023 Model Y. The Tesla was traveling at highway speed on a rural highway with cross traffic and
struck the side of a turning tractor-trailer crossing its path. ODI conducted a post-crash inspection of
the vehicle and crash scene. Based on available information, the tractor-trailer would have been
visible to an attentive driver with sufficient time to avoid the crash.
Another crash occurred in March 2023 in North Carolina and involved a 2022 Model Y. The vehicle
was traveling at highway speed when it struck a minor pedestrian exiting a school bus. The pedestrian
was evacuated by air to a hospital for treatment of serious injuries. Based on publicly available
information, both the bus and the pedestrian would have been visible to an attentive driver and
allowed the driver to avoid or minimize the severity of this crash.

This analysis, conducted before Recall 23V838, indicated that drivers involved in the crashes were not
sufficiently engaged in the driving task and that the warnings provided by Autopilot when Autosteer
was engaged did not adequately ensure that drivers maintained their attention on the driving task.
The drivers were involved in crashes while using Autopilot despite fulfilling Tesla’s pre-recall driver
engagement monitoring criteria. Crashes with no or late evasive action attempted by the driver were
found across all Tesla hardware versions and crash circumstances.
Yaw Loss of Control:
53 crashes were identified where Autosteer was in use in a lower traction environment, such as on
wet roads, where the vehicle lost traction and subsequently directional control, leading to a crash
where the first harmful event was road departure. In these low traction incidents, generally, the
vehicle almost immediately departs the lane after losing lane centering that often results in an impact
with a roadway barrier or other object.
Inadvertent Override:
55 crashes were identified where Autopilot was in use, but it appeared that the driver may have
inadvertently or unknowingly deactivated Autosteer while TACC remained engaged. This happened
either through steering inputs that exceeded the manual override threshold, resulting in the vehicle
drifting out of its lane, or through enough intermittent steering input to change the heading, generally
resulting in a single vehicle roadway departure crash as the first harmful event. Crash factors indicate
that driver disengagement, coupled with the Autopilot system design, is the key contributor to these
crashes. Almost all the incidents (43) were involved in a crash less than 5 seconds after Autopilot was
overridden by a steering torque and the vehicle lost lane centering and departed the travel lane.
Crash Rate and Telemetry
Gaps in Tesla’s telematic data create uncertainty regarding the actual rate at which vehicles
operating with Autopilot engaged are involved in crashes. Tesla is not aware of every crash involving
Autopilot even for severe crashes because of gaps in telematic reporting. Tesla receives telematic
data from its vehicles, when appropriate cellular connectivity exists and the antenna is not damaged
during a crash, that support both crash notification and aggregation of fleet vehicle mileage. Tesla
largely receives data for crashes only with pyrotechnic deployment, 2 which are a minority of police
reported crashes. 3 A review of NHTSA’s 2021 FARS and Crash Report Sampling System (CRSS) finds
that only 18 percent of police-reported crashes include airbag deployments.
Tesla’s telematics also do not fully account for the difference in crash report trends with other L2
systems. A majority of peer L2 companies queried by ODI during this investigation rely mainly on
traditional reporting systems (where customers file claims after the crash and the company follows
up with traditional information collection and/or vehicle inspection). NHTSA has a wide variety of
ways to receive crash reports and ODI did not rely on a simplistic crash rate comparison between
Tesla and its L2 peers based on report counts alone. Rather, ODI also relied on a qualitative review
of the crash circumstances as reported by the Tesla systems, including such information as how long
Pyrotechnic deployment in this case refers to deployment of passive protection systems including air bags, seat
belt pre-tensioners, and the pedestrian impact mitigation feature of the vehicle hood.
3
Tesla’s published Autopilot technology and non-Autopilot technology crash rates are also typically expressed in
miles driven per pyrotechnic deployment.
2

the hazard was visible, whether the crash was reasonably avoidable, and vehicle/driver
performance.
ODI uses all sources of crash data, including crash telematics data, when identifying crashes that
warrant additional follow-up or investigation. ODI’s review uncovered crashes for which Autopilot
was engaged that Tesla was not notified of via telematics. Prior to the recall, Tesla vehicles with
Autopilot engaged had a pattern of frontal plane crashes that would have been avoidable by
attentive drivers, which appropriately resulted in a safety defect finding.
Peer Comparison
Data gathered from peer IR letters helped ODI document the state of the L2 market in the United
States, as well as each manufacturer’s approach to the development, design choices, deployment,
and improvement of its systems. A comparison of Tesla’s design choices to those of L2 peers identified
Tesla as an industry outlier in its approach to L2 technology by mismatching a weak driver engagement
system with Autopilot’s permissive operating capabilities.
Unlike peer L2 systems tested by ODI, Autopilot presented resistance when drivers attempted to
provide manual steering inputs. Attempts by the human driver to adjust steering manually resulted
in Autosteer deactivating. This design can discourage drivers’ involvement in the driving task. Other
systems tested during the PE and EA investigation accommodated drivers’ steering by suspending
lane centering assistance and then reactivating it without additional action by the driver.
Notably, the term “Autopilot” does not imply an L2 assistance feature, but rather elicits the idea of
drivers not being in control. This terminology may lead drivers to believe that the automation has
greater capabilities than it does and invite drivers to overly trust the automation. Peer vehicles
generally use more conservative terminology like “assist,” “sense,” or “team” to imply that the driver
and automation are intended to work together, with the driver supervising the automation.
</reference>

<statements>
1. The DDT encompasses the complete array of real-time operational and tactical functions required to operate a motor vehicle in traffic: operational actuation (steering, braking, accelerating), tactical maneuvering (path planning, object avoidance, turn signaling), and visual-auditory environmental scanning (Object and Event Detection and Response, or OEDR)
2. In an SAE Level 2 configuration—encompassing consumer platforms such as Tesla Autopilot, General Motors Super Cruise, and Ford BlueCruise—the system performs lateral path-centering and longitudinal speed-spacing simultaneously
3. The human must continually scan the operational environment, evaluate downstream spatial hazards, and retain psychomotor readiness to intervene instantaneously
4. A disengaged human supervisor required to intervene during an automated failure cannot re-enter the operational control loop instantaneously
5. Empirical investigations show that while simple mechanical repositioning of the hands on the steering wheel can occur within \(1.5\) to \(2.5\) seconds, the higher-order visual comprehension and spatial orientation necessary to navigate an evolving emergency typically requires between \(5.0\) and \(10.0\) seconds
6. Because highway-speed emergencies typically unfold within a window of \(1.5\) to \(3.0\) seconds, an out-of-the-loop driver cannot execute effective crash avoidance
7. Optical cameras rely on ambient light and clear optical sightlines, leaving them vulnerable to direct solar flare, dense atmospheric fog, heavy precipitation, and high-contrast shadow boundaries
8. Millimeter-wave radar resolves range and relative closing velocities effectively through adverse weather, but radar signal-processing software typically filters out static, unmoving radar returns to prevent false-positive emergency braking on stationary overhead highway signs and bridges
9. When confronted with out-of-distribution artifacts—such as an overturned white trailer crossing a travel lane, a highway crash attenuator whose geometry has been altered by a prior strike, or emergency vehicles parked diagonally across highway shoulders with non-standard flashing light patterns—the automated perception engine frequently experiences classification failures
10. Because the filtering algorithms discard these unclassified obstacles as background noise, the longitudinal control subsystem maintains throttle velocity, navigating into stationary obstacles without issuing an automated brake command or activating a takeover request
11. While tort doctrine accounts for distraction in conventional driving, civil courts presiding over ADAS lawsuits often hold human drivers strictly responsible for inattention, even when that inattention was invited by the vehicle's automated systems
12. Consequently, trial juries often assign primary or complete fault to the human driver under comparative negligence rules, finding that an alert driver would have observed the physical obstacle in time to brake manually
13. In ADAS design defect litigation, plaintiffs focus on the system’s driver engagement and monitoring architecture
14. Plaintiffs argue that marketing an SAE Level 2 system that relies only on passive steering-wheel torque sensors—which measure mechanical resistance but cannot confirm visual attention—constitutes a defectively dangerous design
15. The viable RAD presented in this litigation is a closed-loop, driver-facing infrared camera system capable of continuous optical eye-gaze and head-pose tracking, coupled with hard operational geofencing
16. The engineering cost and operational burden of direct driver monitoring are minimal compared to the foreseeable probability and severity of high-speed collisions caused by driver complacency
17. Because human cognitive disengagement and automation complacency are known consequences of vehicular automation, driver inattention during automated steering constitutes foreseeable misuse rather than an extraordinary intervening act
18. In ADAS litigation, manufacturing defect claims rarely succeed because collision dynamics typically stem from systemic software logic or inherent sensor limitations shared across the vehicle fleet, rather than unique hardware anomalies
19. Administrative defect investigations and modern trial court dockets trace the evolving line of responsibility between machine defects and human error
20. As a key operational factor in the Frontal-Plane Collisions category of EA22-002, the vehicle struck an obstacle or vehicle in its forward path, including stationary first responder vehicles
21. As a telemetric and human factors finding in the Frontal-Plane Collisions category of EA22-002, in 59 crashes with verified visibility metrics, hazards were visible for at least 5 seconds prior to impact; in 19 crashes, visible for at least 10 seconds without evasive steering or braking
22. As a fatality and injury outcome in the Yaw Loss of Control category of EA22-002, the crashes involved road departures and barrier strikes
23. As a telemetric and human factors finding in the Yaw Loss of Control category of EA22-002, the vehicle experienced immediate lateral loss of control following automated lane-centering failure
24. As a fatality and injury outcome in the Inadvertent Override category of EA22-002, the crashes involved single-vehicle roadway departure collisions
25. As a key operational factor in the Inadvertent Override category of EA22-002, the driver manually steered out of lane, deactivating lateral autosteer while cruise control remained engaged
26. As a telemetric and human factors finding in the Inadvertent Override category of EA22-002, drivers failed to recognize that lateral control had disengaged while longitudinal speed was maintained, and crashes occurred within 5 seconds post-override
27. This defect determination resulted in Safety Recall 23V838, requiring over-the-air software updates to strengthen driver monitoring and warning prompts across more than two million vehicles, followed by Recall Query RQ24009 to assess whether these software remedies sufficiently mitigated the underlying risk
28. In Estate of Walter Huang v. Tesla, Inc. (2024), the alleged defect and legal causes of action included wrongful death and strict liability; the vehicle steered toward the highway gore and accelerated to 71 mph into a concrete barrier
29. In contrast, manufacturers have settled cases like Huang when discovery revealed internal engineering awareness of recurring perception errors or when court rulings admitted critical NTSB or NHTSA findings
30. Plaintiffs succeed in surviving summary judgment by pairing human factors testimony with telematics logs to argue that a system's permissive operational envelope predictably invites the precise inattention that leads to the collision
31. Federal safety authorities, including NHTSA, should amend safety standards (such as the FMVSS framework) to prohibit passive, steering-wheel torque monitoring as an acceptable primary method of confirming driver engagement in SAE Level 2 vehicles [5].
32. All Level 2 systems should be required to incorporate direct, closed-loop driver monitoring systems (DMS) using infrared optical cameras capable of tracking eye-gaze direction, eyelid closure, and head orientation [5].
33. When the DMS detects visual distraction or inattention exceeding \(2.0\text{seconds}\), the system must follow a standardized escalation protocol [5].
34. Phase 1: Visual-Acoustic Alert: Gaze diversion from roadway >2.0 seconds
35. Phase 1: Visual-Acoustic Alert: System initiates prominent visual warnings on heads-up display and primary cluster paired with high-frequency chime
36. Phase 1: Visual-Acoustic Alert: Lateral and longitudinal automated assistance remains engaged; system actively scans for driver eye re-engagement
37. Phase 4: Post-Event Lockout: System prevents feature reactivation for the remainder of the current vehicle ignition cycle
38. Phase 4: Post-Event Lockout: Software locks out Level 2 assistance until vehicle is parked and restarted; logs driver monitoring failure
39. Alongside mandatory optical monitoring, regulators should require manufacturers to implement strict Operational Design Domain (ODD) geofencing
40. Level 2 lateral steering assistance should be electronically prevented from engaging on surface streets with unprotected cross-traffic, pedestrian crossings, or non-separated opposing traffic lanes
41. Restricting system engagement to verified access-controlled freeways would prevent the operational mismatches that led to the fatalities examined in *Hsu*, *Riad*, and NHTSA EA22-002
42. To eliminate the evidentiary imbalances that currently complicate civil litigation and regulatory crash investigations, regulatory authorities should establish comprehensive data-logging standards for vehicles equipped with ADAS and ADS platforms
43. These records must log lateral and longitudinal control authority, driver-facing monitoring metrics (including gaze vectors and eyelid tracking), manual driver inputs (steering torque, accelerator pressure, and braking force), automated perception classifications, and the precise timing of any system-issued Takeover Requests or disengagements
44. Following the model of the revised EU Product Liability Directive, state and federal courts should apply a rebuttable presumption of product defectiveness whenever an automotive OEM fails to maintain or produce required DSSAD crash logs, or when an ADAS-equipped vehicle collides with a stationary obstacle within its documented design envelope without issuing an automated brake command or timely Takeover Request
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.