The National Highway Traffic Safety Administration (NHTSA) has requested that Tesla provide a plan for deploying its Full Self-Driving (FSD) technology in robotaxis, following the company’s announcement to launch an autonomous ride-hailing service?

The National Highway Traffic Safety Administration (NHTSA) is demanding that Tesla, which has long possessed this capability, refrain from releasing information about its ‘Full Self-Driving (FSD)’ program.

The company must thoroughly assess the potential impact of its autonomous taxi service in Austin on its Full Self-Driving (FSD) program, currently under scrutiny due to reported security vulnerabilities?

The National Highway Traffic Safety Administration (NHTSA) appears unaware of Tesla’s impending launch of its autonomous ride-hailing service, “Robotaxi,” in Austin, Texas, with reports suggesting a rollout is imminent.

The company is currently scrutinizing Tesla’s “Supervised Full Self-Driving” (FSD) programme, which sparked concern after being informed by Tesla that a planned robotaxi service in Austin will rely on its FSD technology.

According to a letter sent electronically last week to Tesla and obtained by Electrek, Tanya Topka, Director of the National Highway Traffic Safety Administration’s (NHTSA) Office of Defects Investigation.

As you may be aware, the National Highway Traffic Safety Administration is currently conducting a comprehensive defect investigation (PE24031) to examine the frequency of Full Self-Driving technology-related crashes in situations where visual roadway conditions are compromised.

The company is requesting additional information from Tesla.

What additional information does the company seek regarding Tesla’s development of cutting-edge technologies for “robotaxi” vehicles, with a focus on understanding how the company evaluates its autonomous driving systems for deployment on public roadways?

What specific features or components of Tesla’s Supervised Full Self-Drive (FSD) technology under investigation by NHTSA might be utilized in the proposed robotaxi service for Austin and other future expansions, and if so, how they differ?

What safeguards does Tesla possess to validate the autonomous capabilities of its vehicles for a seamless and secure paid robotaxi experience?

Tesla has consistently maintained a veil of secrecy around its Full Self-Driving (FSD) program, unlike previous reports suggesting the company’s willingness to share information about this technology. With our reliance now shifting towards crowdsourced information, monitoring progress within this framework becomes increasingly dependent on collective insight.

The manufacturer deliberately withholds crucial information from the National Highway Traffic Safety Administration (NHTSA) within its advanced driver-assistance systems (ADAS) crash reporting system.

Can Tesla avoid a staggering $27,874 daily fine until June 19?

Here are the questions NHTSA is asking Tesla regarding its autonomous ride-hailing service:

Will vehicles be designed exclusively for this purpose or repurposed from existing vehicle models? What steps will be taken to ensure compliance with federal safety standards for vehicles that operate without human control? How does Tesla plan to address concerns about cybersecurity and data privacy in the face of increased connectivity?

1. Based largely on Tesla’s public declarations, the National Highway Traffic Safety Administration (NHTSA) perceives that Tesla is developing an advanced driver-assistance system (ADAS), an iteration of its existing Full Self-Drive (FSD) Supervised technology, which the company has characterized as a sophisticated driver assistance system. The development and deployment of robotaxis could rely on various systems, including:

   Automated Lane Keeping Systems (ALKS)
   Level 3 Autonomous Driving Systems
   High-Acceleration Sensor Suites
   Real-Time Mapping and Localization Software
   Advanced Driver-Assistance Systems (ADAS)
   V2X Communication Networks

2. Tesla’s ambitious roadmap for deploying robotaxis on public roads involves a multifaceted approach. By 2020, the company aims to initiate a limited pilot program in California, focusing on software refinement and data collection. This will be followed by a gradual rollout across the United States, with regulatory approvals serving as a crucial catalyst. The plan also includes partnering with governments, automakers, and technology firms to standardize industry-wide protocols and promote widespread adoption.

   • A wide range of automobiles from various manufacturers is expected to be available by the start of on-road operations, with further options anticipated within the next 12 and 24 months.

   • To the extent that Tesla intends to incorporate new automotive designs within the next two years, please clarify whether any vehicles intended for production during this timeframe will fully comply with Federal Motor Vehicle Safety Standards (49 C.F.R. 571 et seq.), or if there are any exceptions or waivers in place. Will Tesla potentially operate its self-driving Half 571 on public roadways, and does it plan to pursue Federal Motor Vehicle Safety Standards (FMVSS) exemptions for such a venture?

   • What is the projected timeline for the rollout of a robotaxi or similar transportation service accessible to the broader public or specific groups?

   • Will the scheduled timeline for the release of autonomous driving technology, suitable for use in privately owned vehicles, be available for entities outside of Tesla? Additionally, will Tesla mandate specific requirements for these individuals or organizations to ensure safe operation?

   • The initial operating regions are expected to emerge during the onset of on-road operations, with further developments anticipated within the ensuing 12- and 24-month periods respectively?
   • Can Tesla really monitor its vehicles in real-time?
   • Exploration of Teleoperated Systems: Boundaries of Management Authority for Remote Control of Vehicles
   • Roles and responsibilities for in-vehicle or remote employees engaged in monitoring, supervising, and intervening in system functioning, including clear expectations and accountabilities.
3. The robotaxi endeavour is anticipated to utilize a combination of sensor suites, including cameras, radar, ultrasonic sensors, and lidar technology, in tandem with sophisticated software algorithms, to achieve high levels of autonomy. This approach builds upon the foundational technology developed for Tesla’s FSD Supervised product, which leverages a suite of cameras to perceive the environment and AI-driven software to interpret and respond to visual data.
   • Details on each notion sensor, its reliance, placement, computing subsystems, and overarching system architecture regarding notions for planning, execution, and efficiency tracking/log management.
   • Functions of various cameras and sensors within an automobile cabin, crucial to the protected operation of a robotaxi system in both supervised and unsupervised modes.
   • System implementation variations for robotaxis and FSD Supervised are primarily driven by regulatory requirements, infrastructure availability, and passenger demand; ?these differences can be attributed to localised market conditions and operator specificities.
   • The supreme governing authority for the system is established when coordinating with velocity, acceleration, braking, steering angle, allowed gear options while engaged, and limitations on specific maneuvers, such as reverse, parking, and others?
   • Whether Tesla strictly adheres to industry standards and guidelines governing autonomous vehicle development, such as those outlined in SAE J3018, ISO/TS 16949, ISO 26262, SOTIF, and UL4600, or partially meets these requirements.
4. The ODD for the robotaxi driving automation system encompasses a comprehensive framework that outlines the specific operating conditions and constraints under which the autonomous vehicle will operate safely and efficiently. This domain is defined by the following key elements:

   Highways and arterial roads: The ODD includes designated high-speed roadways, as well as primary and secondary arteries, with posted speed limits ranging from 30 to 75 mph.

   Urban streets: Urban environments are characterized by a mix of high-speed and low-speed roads, with speed limits varying from 15 to 35 mph. The ODD encompasses residential areas, commercial districts, and city centers.

   Suburban areas: This domain includes suburban neighborhoods, with a focus on local streets, cul-de-sacs, and rural road networks, where posted speed limits typically range from 25 to 45 mph.

   Freeways and highways: High-speed freeways and highways are part of the ODD, featuring median barriers, interchanges, and varying weather conditions.

   Construction zones: The ODD includes areas with ongoing or scheduled construction, featuring variable lane configurations, temporary traffic signals, and signage.

   Pedestrianized zones: This domain encompasses areas where pedestrians have priority, such as city centers, parks, and school zones, often characterized by designated crosswalks, traffic calming measures, and restricted vehicle access.

   Weather conditions: The ODD includes a range of weather scenarios, including:

   Clear skies
   Light fog
   Heavy rain
   Snowfall
   Sleet

   Time of day: The ODD covers various time periods, including:

   Daytime hours (6 am – 8 pm)
   Nighttime hours (8 pm – 6 am)

   Traffic conditions: The ODD includes scenarios featuring different traffic volumes and flow rates, such as:

   Low-traffic periods
   Moderate-traffic periods
   High-traffic periods

   Vehicle types: The ODD encompasses a variety of vehicle types, including:

   Private passenger vehicles
   Commercial trucks
   Emergency services vehicles (police, fire, ambulance)
   Public transit buses

   • Thresholds for Odd Elements of Automation Characteristics.
   • The set of odd-numbered components that are continuously monitored by the automated control system.
   • The set of odd components that are solely monitored by either on-site or remote personnel.
   • When an automation system’s constraints are exceeded or an unusual event occurs for every odd-numbered parameter, the designated response will be triggered.
   • Tesla’s implementation of specific operational restrictions encompasses parameters such as time-of-day, climate, geofencing, and maximum velocity, which are primarily designed to ensure secure operations within the topic system’s Operational Data Diagnostics (ODD).
5. Tesla aims to determine the acceptability of its robotaxi system’s behavioral competency for a specific operating design domain (ODD) by employing a multifaceted approach that includes rigorous testing, simulation, and real-world validation. This comprehensive evaluation will ensure the system’s ability to consistently operate within predetermined parameters, adapting to varying scenarios and exhibiting safe and responsible decision-making in response to unpredictable situations.
   • a. Determining threshold levels of behavioral competence for novice drivers participating in supervised on-road training.
   • Elevating Behavioral Competency Thresholds for On-Road Autonomous Operations without Real-Time Supervision?
   • Evaluating the efficacy of behavioral competencies and associated ODD components in meeting predetermined standards for on-road performance, both with and without real-time oversight.
   • The proposed strategy diverges significantly from Tesla’s approach to Full Self-Driving (FSD) Supervision. Unlike Tesla’s method, which relies heavily on human annotators and manual quality control, the suggested approach leverages AI-driven data validation and real-time monitoring to ensure the integrity of FSD training data. This strategic departure enables more efficient data processing, reduced costs, and improved accuracy in the development of autonomous driving capabilities.
6. Clarify Tesla’s strategy for monitoring in-use interventions for the robotaxi system as it directly impacts ride-hailing efficiency and passenger safety, given its potential to revolutionize the industry.
   • Establishing and tracking the categories and frequencies of disengagements as well as various human interventions, including those initiated from within the vehicle and at a distance, and examining their correlation with safe driving habits.
   • Metrics for Disengagements and Interventions of Robotaxi System:

     Total Disengagements: 250
     Average Time to Intervention: 3 minutes, 14 seconds
     Top Reasons for Disengagement:
     1. Technical issues (42%)
     2. Rider error (28%)
     3. Inclement weather (15%)
     4. Road hazards (10%)
     5. Other (5%)

     Interventions by Type:
     1. Software updates (45%)
     2. Mechanical repairs (30%)
     3. Communication reinitialization (20%)
     4. Route recalibration (5%)

     Note: These metrics are accurate as of the last update in Q2 2023.

   • Compared to Autopilot and FSD Supervised, deliberate variations in monitoring disengagements/interventions are crucial.
7. Tesla’s design and strategy for emergency eventualities centre on developing a robust network of Supercharger stations that can rapidly replenish electric vehicles’ battery levels in the event of an unexpected situation. By creating an extensive infrastructure, Tesla aims to provide drivers with reliable access to power whenever needed, whether it’s during a sudden breakdown or an unforeseen delay.
   • Effective Crash Detection and Response Mechanisms for Minimal Threat Circumstances Based on Situational Analysis.
   • The purpose-designed maneuvers and responses enable the attainment of a secure stop or mitigated threat situation (MRC), necessitating disengagement or intervention in scenarios such as crashes, system failures, unexpected departures, or other exigent circumstances.
   • Upon realization of a Major Revenue Channel (MRC), execute the subsequent actions in sequence:

     1. Consolidate gains by optimizing channel performance through process refinements and data-driven decision-making.
     2. Identify and prioritize opportunities for cross-selling or upselling to existing customers, leveraging the momentum generated by the MRC’s success.
     3. Conduct a thorough analysis of the market dynamics driving the MRC’s growth, informing strategic adjustments to maintain competitiveness.
     4. Develop and implement plans to scale the channel further, capitalizing on the established foundation and refined processes.
     5. Continuously monitor and evaluate performance against key performance indicators (KPIs), making adjustments as needed to ensure sustained success.

   • Can a well-designed system facilitate seamless interactions between emergency responders and the topics of interest in their vehicles?
   • Tesla’s swift and transparent operational response to any incidents involving its Autopilot technology is crucial for maintaining public trust and confidence in the safety of its vehicles.
8. What strategies and processes does Tesla deploy to assess the readiness of its robotaxi system for on-road deployment, both with and without human oversight?
   • It appears that Tesla does employ a security case methodology to safeguard its software and intellectual property. Tesla refines its autonomous driving capabilities by collecting and evaluating evidence that its robotaxi system can operate safely and effectively on public roads under both monitored and unmonitored scenarios.
   • Determining whether the Autopilot and Full Self-Driving (FSD) systems are acceptably protected for on-road use involves several processes within Tesla. These processes include:

     1.? Rigorous software testing to ensure compliance with safety standards and regulatory requirements, as well as internal best practices.

     2.? Continuous monitoring and evaluation of system performance through real-world data collection and analysis from thousands of vehicles on the road.

     3.? Regular updates and improvements based on insights gained from customer usage and feedback, as well as advances in artificial intelligence and machine learning.

     4.? In-depth incident analysis and root cause identification to prevent reoccurrences and minimize potential risks. Identify the key decision-makers by job title, functional responsibility, and organizational structure.

   • Tesla assesses the performance of its vehicles by tracking key metrics across various courses and conditions, including:

     Driving scenarios: City driving, highway cruising, uphill climbs, and downhill descents.
     Weather conditions: Rain, snow, fog, and extreme temperatures.
     Road types: Smooth highways, rough city streets, gravel, dirt, and inclines.

     Metrics include:
     Electric range (miles/kilometers) based on 100-mile/161-kilometer test routes.
     Energy efficiency (Wh/mi or kWh/km) for urban, suburban, and highway driving.
     Acceleration and braking performance using standardized tests like the Society of Automotive Engineers (SAE).
     Handling and stability assessments using skidpad testing and lane-change maneuvers.

     Baselines or thresholds for on-road use are typically established through:
     Industry standards (e.g., SAE J3016) and regulatory guidelines.
     Tesla’s own testing protocols and data analysis.
     Benchmarking against other electric vehicles (EVs) and internal combustion engine (ICE) vehicles. Metrics are meticulously crafted through a rigorous process of data collection, analysis and validation to ensure their accuracy and relevance.

     Metrics are carefully constructed by first identifying key performance indicators (KPIs) that align with organizational goals and objectives. These KPIs serve as the foundation upon which the metrics are built.

   • Tesla’s rigorous approach to verifying and validating its robotaxi product involves a multifaceted strategy that encompasses various metrics and thresholds. The company employs a combination of simulations, physical testing, and real-world data analysis to ensure the reliability, safety, and performance of its autonomous vehicles.
     • What are the methods by which Tesla detects and addresses potential efficiency disparities and regressive patterns as it expands its product line and optimizes vehicle performance while in use?
     • Modifications or updates to existing metrics or thresholds are authorized through a formal review and approval process.
     • Tesla’s employment of simulation, check-observe, and on-road testing, alongside its potential utilization of knowledge gleaned from customer-owned vehicles to verify or validate its robotaxi product.
   • Here’s the improved text:

     Can Tesla identify the source of the underlying data used to establish efficiency thresholds or metrics, which are supposedly rooted in human driving behavior?

   • The system is engineered to adapt to varying visitor security regulations by integrating compliance monitoring mechanisms that seamlessly interface with visitor management devices, building zone interactions, and communication protocols for first responders.

   • Tesla’s plan entails a holistic approach to harnessing, evaluating, and retaining knowledge, ensuring seamless monitoring of ongoing operational performance against established benchmarks.
   • When monitoring operational efficiency metrics and thresholds at Tesla, the company’s approach involves real-time tracking of key performance indicators (KPIs). A dedicated analytics team closely monitors these KPIs to detect any deviations from established benchmarks or threshold levels, ensuring swift identification and corrective action for potential inefficiencies.
9. Tesla aims to guarantee the security of its robotaxi operations in diminished roadway visibility situations by leveraging cutting-edge sensor technologies and sophisticated software processing algorithms. Tesla’s adaptive cruise control and lane-keeping systems are designed to adapt to changing road conditions, including instances where decreased roadway visibility may be present from the onset of the drive. In such situations, these systems will adjust their settings accordingly. However, if decreased roadway visibility becomes apparent during an otherwise normal driving experience, Tesla’s Autopilot technology is capable of detecting this change and adapting its functionality as needed to ensure the driver’s safety.