In this post, we will look at four critical definitions of autonomous vehicles (AVs) provided in laws and regulations that have been enacted to date. These definitions help us understand how different jurisdictions are approaching the integration of AV technology.

Nevada

Enacted: June 2011, revised July 1, 2013.

Definition of AVs: “Autonomous technology” means technology which is installed on a motor vehicle and which has the capability to drive the motor vehicle without the active control or monitoring of a human operator. The term does not include a dynamic safety system or a system for driver assistance, including, without limitation, a system to provide electronic blind-spot detection, crash avoidance, emergency braking, parking assistance, adaptive cruise control, lane-keeping assistance, lane departure warning, or traffic jam and queuing assistance, unless any such system, alone or in combination with any other system, enables the vehicle on which the system is installed to be driven without the active control or monitoring of a human operator.

Update:

Nevada now distinguishes between levels of automation based on the SAE International J3016 standard.

• Commercial Autonomous Vehicles and Delivery Robots: Nevada has expanded its legislation to include specific regulations for commercial autonomous vehicles and delivery robots. This includes defining operational zones, permissible hours of operation, and safety standards for these vehicles. For instance, delivery robots are now allowed to operate on sidewalks and crosswalks under certain conditions, with weight and speed limits to ensure pedestrian safety.
• Cybersecurity and Data Privacy: Recognizing the importance of protecting AVs from cyber threats, Nevada has introduced comprehensive cybersecurity requirements. These include mandatory encryption for data transmission, regular security audits, and protocols for real-time threat detection and response. Additionally, data privacy regulations mandate that all data collected by AVs must be anonymized and used strictly for improving vehicle performance and safety, with strict penalties for non-compliance.

Florida

Enacted: April 2012.

Definition of AVs: “Autonomous technology” means technology installed on a motor vehicle that has the capability to drive the vehicle on which the technology is installed without the active control of or monitoring by a human operator (Florida Statutes, 2012). Excludes vehicles “enabled with active safety systems or driver assistance systems, including, without limitation, a system to provide electronic blind-spot assistance, crash avoidance, emergency braking, parking assistance, adaptive cruise control, lane keep assistance, lane departure warning, or traffic jam and queuing assistant, unless any such system alone or in combination with other systems enables the vehicle on which the technology is installed to drive without the active control or monitoring by a human operator” (Florida House of Representatives, 2012).

Update:

Florida’s definition now incorporates the SAE J3016 levels of automation and emphasizes the importance of cybersecurity and data recording in AV operation.

• Vulnerable Road Users: Florida has introduced new measures to ensure the safety of vulnerable road users, such as pedestrians and cyclists. AVs must now be equipped with advanced detection systems capable of identifying and responding to these users in real-time. Additionally, there are strict guidelines on how AVs should interact with school zones, pedestrian crossings, and bicycle lanes.
• Compliance with Federal Safety Standards: All AVs operating in Florida are required to comply with the latest federal safety standards, including those set by the National Highway Traffic Safety Administration (NHTSA). This includes rigorous testing and certification processes to ensure that AVs meet high safety and performance benchmarks before they can be deployed on public roads.

California

Enacted: September 2012.

Definition of AVs: “‘Autonomous technology’ is defined as technology that has the capability to drive a vehicle without the active physical control or monitoring of a human operator” (California Vehicle Code, 2012). “Autonomous vehicle” means any “vehicle equipped with autonomous technology that has been integrated into that vehicle. Does not include a vehicle that is equipped with one or more collision avoidance systems, including, but not limited to, electronic blind-spot assistance, automated emergency braking systems, park assist, adaptive cruise control, lane keep assist, lane departure warning, traffic jam and queuing assist, or other similar systems that enhance safety or provide driver assistance, but are not capable, collectively or singularly, of driving the vehicle without the active control or monitoring of a human operator.”

Update:

• Ride-Sharing and Ride-Hailing Services: California has enacted specific regulations for autonomous ride-sharing and ride-hailing services. Companies like Uber and Lyft, which are integrating AVs into their fleets, must adhere to stringent safety and operational guidelines. This includes mandatory driver monitoring systems, regular maintenance checks, and clear protocols for passenger safety and emergency situations.
• Data Sharing and Transparency: To ensure public trust and safety, California requires AV companies to share data related to vehicle performance, incident reports, and software updates with state regulators. This transparency allows for continuous monitoring and improvement of AV technology. The state has also established a public database where citizens can access information about AV operations and safety records.

Washington, D.C.

Enacted: January 2013.

Definition of AVs: “A vehicle capable of navigating District roadways and interpreting traffic-control devices without a driver actively operating any of the vehicle’s control systems.” “Excludes a motor vehicle enabled with active safety systems or driver assistance systems, including crash avoidance, provide electronic blind-spot assistance, emergency braking, parking assistance, adaptive cruise control, lane keep assistance, lane departure warning, or traffic jam and queuing assistance, unless a system alone or in combination with other systems enables the vehicle on which the technology is installed to drive without active control or monitoring by a human operator” (District of Columbia, 2013).

Update:

• Autonomous Public Transportation: Washington, D.C. has implemented regulations to facilitate the integration of AVs into the public transportation system. This includes autonomous buses and shuttles that operate on predefined routes. These vehicles must comply with rigorous safety standards and are subject to regular inspections and performance evaluations.
• Ethical Decision-Making Algorithms: The legislation now addresses the ethical considerations and decision-making algorithms used by AVs. This involves setting standards for how AVs should prioritize the safety of passengers, pedestrians, and other road users in complex scenarios. The regulations also mandate transparency in the development and implementation of these algorithms, ensuring that ethical considerations are consistently applied.

The legal landscape surrounding autonomous vehicles has evolved rapidly to keep pace with technological advancements. As AVs become more prevalent, ongoing legislative efforts will be crucial to ensure their safe and responsible integration into our transportation systems. By continuously updating and refining these definitions, lawmakers are ensuring that legislation keeps pace with technological advancements, promoting the safe and efficient integration of autonomous vehicles into society.

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The aim was to monitor and encourage business directors to meet a collection of checkpoints as a requirement for clean financial audits. Regulatory compliance is not just a legal obligation but is considered good business practice.

When it comes to SOX compliance regulations, the dimension of cloud identity falls within section 404: Management Assessment of Internal Controls. According to this section of SOX, management is responsible for creating adequate internal control structures, including measurement tools to assess the effectiveness of such controls. The financial report should also clearly define any shortcomings discovered using the predefined controls.

Cloud Identity Checklist

As a starting point, any organization that operates using cloud-based services and, by extension, cloud-based identities should start by clearly listing and defining all the identities in use. These will include identities such as Enterprise Singe Sign-On identities, OpenID identities, and identities utilizing SAML. Depending on the vendor providing the cloud platform, the identities might be among the following: AWS IAM, Microsoft Azure identities, or Google cloud identities.

To preemptively address compliance areas concerning IT and any identities, whether localized or in the cloud, it is always a good idea to create a SOX identity management checklist. It will aid the organization in securing system assets and aligning practices with healthy record-keeping standards. Here are some examples of compliance checklist items that can be considered:

1. Inhibit manipulation of system data

Cloud ecosystems should be designed in such a way that it protects the system from fraudulent activities. A good starting point would be tracking any authentication activities by cloud identities, especially on financial systems.

2. Log timelines of critical activities

By utilizing logging algorithms that track feedback from specific metrics or datasets. Critical activities can be logged this way for scrutiny. This could, for example, be logging specific lambda executions between encryption of data at rest and encryption of data in transit. In the case of cloud identities, it might be logging user activities.

3. Design dependable data controls

Controlling the “who” and the “why” surrounding data access will greatly reduce the chances of cloud identities gaining access to information they are not privileged to. The best practice for these controls is to base the controls on the least privileged design.

4. Highlighting safeguards to auditors

Al the safeguards that have been put into place to ensure compliance of online identities should be communicated to auditors, who might not be technically inclined, in such a way that it is understandable. Some organizations even generate daily online identity audit reports for precisely this purpose.

5. Effective breach detection

From your compliance checklist, it should be clear what your contingency steps are in the case of an Identity-based breach. The best practice in this area would be to have an incident management team dealing with these occurrences.

Third-Party Solutions

Luckily organizations don’t need to face this behemoth of a task on their own. Third-party vendors exist that offer solutions that aid organizations in setting up these policies and even offer real-time monitoring of such directives on behalf of the organization. This frees the organization from having to employ audit and compliance specialists. External vendors also offer automated real-time monitoring of these online identities, highlighting possible breaches before they happen.

Conclusion

For any organization, big or small, the matter of regulatory compliance should be a serious one. The ability of an organization to meet the security standards to satisfy the benchmark requirements, as laid out by SOX, brings peace of mind. This piece of mind applies to both the organization and its shareholders. The compliance regulations surrounding online identities boil down to instilling sufficient control and monitoring policies and practices that are achievable and realistic. Establishing effective compliance policies and honoring them will inevitably go a long way towards creating a positive reputation for the organization in the marketplace.

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Luckily, to a great extent, proper law enforcement and transparency can act as modifiers to these potential challenges without disrupting the further development of AI.

Therefore, analyzing the legal implications of using data for AI is important. The access and the use of data can be limited in two ways: either by privacy laws or by intellectual property rights that protect the commercial value of data for firms and individuals.

These limitations or restrictions aim to balance open data access that enables the development of AI tools while also respecting the rights of data holders and data subjects. These rights should be respected by people and companies using AI products and fully automated algorithmic tools operating without human intervention.

Privacy and data protection

Privacy and data protection laws require that certain safeguards that protect individuals are in place to allow the free flow and utility of data. These rules vary among different countries and regions, but they may generally limit access afforded to AI companies to datasets containing personal data.

According to the GDPR, for example, the use of such datasets must comply with a set of data protection principles (i.e., processing for a specified and legitimate purpose, data minimization, consent). Likewise, privacy regulations require companies to give people access to and information about the processing of their data.

Compliance with these requirements in an AI context is difficult, and the lack of explicit answers to AI-related privacy issues leads to uncertainties and costs for AI companies. Increased costs may then limit their access to data and hinder the development of AI tools.

Despite some similarities, privacy regulations worldwide follow different approaches to data access and data use due to the diverse understanding of privacy. The lack of a unified global data privacy regime can also hinder data flow beyond jurisdictional borders. Harmonizing regional and domestic privacy frameworks via international collaboration can make data rapidly and equitably available to develop AI-driven tools. Otherwise, the development of AI tools will depend on data availability in each separate region or country.

The differences in data access regulation across regions can also influence the quality and competitiveness of AI tools. AI companies may be interested in developing their tools in countries with less restrictive privacy laws. Fewer privacy restrictions enable AI development by increasing data availability and reducing compliance costs.

Intellectual property rights (IPR)

Intellectual property rights, specifically copyright and database rights, are another legal framework that directly impacts AI data access. These rights have the potential to both enable and hinder AI development.

One of the main reasons is that intellectual property rights, particularly rights in databases, are treated differently in national law, with significant differences in the level of protection for databases. While international laws apply, they only establish minimum protection standards that must be guaranteed in domestic law. We can look at database rights to understand how IP regulations affect data accessibility.

In the United States, a database can only be protected with compilation rights, which give it copyright if the data has been “selected, coordinated, or arranged in such a way that the resulting work as a whole constitutes an original work of authorship.” Compilation rights, in essence, only protect databases that have been assembled or curated with a minimum level of originality or creativity; they do not provide protection to a creator solely because of the time and/or effort put into a database’s creation.

Databases that aren’t eligible for compilation rights aren’t protected by IP laws in the United States, and creators can’t legally prevent others from accessing or using them. Australia, Brazil, China, Hong Kong, Japan, and Singapore are the countries that only provide this level of protection. As a result, it is more difficult to protect databases created in these countries under IP law, and unprotected databases can be accessed more easily for AI development.

Sui generis database rights are a type of protection that exists in Europe in addition to compilation rights. The creator’s investment in obtaining, verifying, or presenting the database’s contents is protected by a sui generis database right. To be eligible for protection, the investment must use a significant amount of resources and/or effort, either qualitatively or quantitatively. In essence, this right recognizes and protects the time and resources invested in the creation and/or maintenance of a database. This type of broader security allows the owner to prevent others from accessing the database, potentially limiting how AI can be developed using the data.

A protected database can be used for legal purposes, but it must be identified in the law (e.g., the sole purpose of illustration for teaching or scientific research). Similar safeguards are in place in India, South Africa, and South Korea. Databases created in these countries are easier to protect under intellectual property law and may be more difficult to access for AI development.

However, it should be noted that IP law is not the only way for a database creator to protect his or her work. Creators can lawfully restrict access to a database through contract law, such as licensing or confidentiality provisions, in both types of legal regimes – with or without sui generis database rights.

Therefore, the existence or lack of different legal frameworks governing access and rights to data and datasets can significantly impact their availability to develop and use AI tools. Stricter privacy and IP rules can limit the use of data. Still, they can also create incentives for individuals and organizations to share their data or to engage in building new tools and/or datasets. The lack of harmonized global privacy or IP legal regimes may hinder the cross-border flow of data and, thus, restrict the development of AI at a regional or national level. Providing clarity to the rights of data holders and establishing concrete rules governing data sharing among public and private actors can increase the flow and availability of data for AI.

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A collaborative environment brings together both humans and robots’ best qualities to reduce manufacturing costs and time. It allows workers to focus on operations with high added value or demanding high dexterity levels, thus freeing them from repetitive or potentially risky tasks.

However, one of the key questions in human-robot collaboration is safety. Any harm caused by a robot due to an unexpected motion or behavior is not limited only to the worker’s physical injury but also has a financial impact on insurance costs, lost production, damaged machine, lost customers, and even loss of reputation of the company. To prevent this, it is important to identify, plan and supervise safety aspects from the start, at the designing phase, until the decommissioning and scrapping of the robot.

The ISO 10218 “Robots and robotic devices – Safety requirements for industrial robots” (Parts 1 and 2) and the ISO/TS 15066 “Robots and robotic devices – Collaborative Robots” clearly describe four types of safety methods to ensure safety during collaborative situations. Those methods are safety-rated monitored stop, hand guiding, speed and separation monitoring, and power and force limiting.

1. Safety-rated monitored stop method (SMS)

The safety-rated monitored stop method means pausing a collaborative robot’s motion before an operator enters the collaborative workspace. When the safety-rated monitored function is active, the robot maintains power but does not move. The robot resumes the motion without any additional intervention, only after the operator is no longer present in the collaborative workspace. The robot starts back up automatically and works at high speeds, while the workspace is clear of operators.

For the safety-rated monitored stop function to work, the collaborative robot needs to know when an operator is entering the collaborative workspace. If an operator enters the collaborative workspace while the robot moves, the robot generates a protective stop. The operator must exit the workspace and reset the system before the robot can restart. This method requires software or a device to pause a robot’s function when the worker is coming closer to the robot to prevent dangerous motion.

2. Hand guiding method (HG)

The hand guiding method refers to moving a robot system by a hand-operated device to transmit motion commands. In this, the robot receives the stop command directly from the operator, using a hand-operated device located at or near the robot’s end-effector. The device transmits the motion commands to the robot. This allows a cobot to move through direct input from an operator. The cobot stays in safety monitored stop until an operator actuates the hand guiding device through an enabling switch. Hand-guided cobots can work autonomously when operators aren’t around.

3. Speed and separation method (SSM)

Also known as operational speed and worker-robot separation monitoring methodology (SSM), this is one of the available methods to reduce the risk of injury according to the ISO technical specification 15066 on a collaborative robot in a sharing workspace.

It ensures safety by specifying the minimum protective distance between a robot and an operator. The robot can move concurrently with the operator as long as they maintain a pre-determined distance apart. A safety laser area scanner constantly monitors at least the protective separation distance. When in motion, the robot never gets closer to the operator than the protective separation distance, which often varies based on how fast the cobot is moving.

4. Power and force limiting method (PFL)

The power and force limiting method allows the contact between an operator and a robot either intentionally or unintentionally. Still, it restricts/controls the robot’s speed, torque, motion to avoid any injury and pain. The threshold power and force limit values are determined in a risk assessment. The robots are specifically designed with power or force feedback built in to detect contact with a person.

Power and force limiting safety functions enable collaborative applications where contact with people is permitted when contact pressure and forces are acceptable. The PFL function will suspend the robot when it detects an external force exceeding the preset limit value due to contact or a collision between the robot and a worker or the robot and an obstacle.

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The implementation of CAVs is a big opportunity for economic development. Still, it also poses important questions about how to maximize the technology’s benefits to social welfare while mitigating negative externalities. Studies show that even partial implementation could have dramatic impacts on our transportation infrastructure and travel patterns.

Although the full deployment of CAVs remains years away, it is time, therefore, for government officials, planners, and economic developers to begin preparing for the potential impacts of this transformative technology and to carefully consider how the potentially substantial changes may dramatically change transportation, infrastructure, and land use.

The local agencies need to plan the most likely scenarios for CAVs in the next 10 to 5 years, including gradual integration of autonomous technology, truck platooning, and the critical infrastructure needs like pavement markings, signing, traffic signals and communication infrastructure, high-resolution mapping of roads, maintenance, consistency and standardization of rules, etc.

In this post, we briefly present some of the key recommendations that government and road agencies should consider as part of autonomous vehicles readiness and prepare for the potential policy and implications of CAVs.

1. Pavement markings

Current CAV systems use cameras and image processing to identify lane markings to the road alignment and locate the vehicle within the cross-section of the road. Several demonstrations of autonomous vehicles have failed due to the overlapping and inconsistent pavement markings, making it difficult for the sensors to predict where the vehicle is in the lane, causing the vehicle to rely on other features such as the edge of the roadway. This underscores the need for consistency of markings for current advanced driver-assistance systems (ADAS), and lane-keep assistance (LKA) to decipher the road surface and keep a vehicle positioned within its lane.

Therefore, road agencies should track how autonomous vehicle manufacturers are handling CAV lane-keeping technology. The direction in which the CAV lane-keeping technology goes will impact pavement marking policies, signage development, and rehabilitation, which will cost road agencies more in maintenance costs. The popular recommendations are as follows:

• Create national standards and guidance on pavement markings and standards that will include minimum criteria of a 6-inch edge, lane, and center lines with a retro-reflectivity level of 35 mcd.
• Develop a high contrast and high retroreflective pavement marking tape or liquid pavement markings with 3M Optics that make markings more visible in the rain or other low-visibility conditions.
• Update and maintain good-quality, consistent, and uniform lane markings.
• Place lane lines immediately after constructing or resurfacing roads.

2. Signing

All CAVs use optical cameras for sign recognition. The machine-vision systems capture an image, such as a street sign, and then classify the sign using feature extraction and matching. Similar to pavement markings, current CAV systems are confused by damaged, faded, or non-compliant signs. CAVs require signing to be consistently placed and maintained at a much higher level than the current practice.

Notably, 3M has developed signs with embedded smart codes that can be read by a vehicle sensor. The information is encoded using special films and inks, which infrared cameras can read. Smart codes can refer the vehicle back to a central database with regularly updated information about the road. Other sign technologies may include Bluetooth beacons or radio frequency identification devices (RFID) to make signage machine-readable. Several recommendations are suggested that can assist local agencies in preparing for CAVs.

• Remove non-standard, blocked, damaged, or faded signing.
• Consistent use, proper maintenance, and replacement of signs
• When available, consider the use of signs with smart codes, which can be read by a vehicle with an infrared (IR) light source at a distance. The codes should include information such as GPS coordinates, sign installation dates, maintenance dates, etc.
• Implement diamond-grade reflective sheeting, which makes the signing readable over a range of angles and during inclement weather.

3. Traffic signals & communication

Along with pavement markings and signing, CAVs will need to detect and identify traffic signals that generate Signal Phase and Timing (SPaT) messages, including green, yellow, red, and the amount of time left until the next phase. Several studies recommend implementing dynamic message system (DMS) signs and new traffic signal controllers that have an internet protocol (IP)-ready ports and National Transportation Communications for Intelligent System Protocol (NTCIP) compliance for full-scale CV deployment and the ability for integration into advanced traffic management systems (ATMS).

The simplest recommendation is to ensure enough space for additional hardware and communications during the replacement of traffic signal controllers. Additionally, agencies will need to work with vendors to determine which communications technologies will be required to allow vehicles to send and receive data in conjunction with signal controllers as technologies evolve. They also need to consider future communication needs as part of roadway plans.

4. Maintenance

Maintenance is a key aspect of accommodating AVs. It refers to pavement and winter weather maintenance of any roadway. Significant degradations in roadway surface conditions pose a considerable concern for CAVs since it is uncertain how imaging systems may interpret these surface conditions. It can also make it difficult for a CAV to predict the behavior of other road users.

Additionally, the presence of snow and rain obscure pavement markings and signing, making it difficult for camera systems to detect and interpret them. Therefore, it is necessary to maintain roads to a higher standard than for human drivers. At this point, it is unknown the extent to which additional maintenance may be needed to accommodate CAVs. However, the best recommendation is to maintain or implement timely maintenance, mainly when significant surface degradation, such as potholes, occurs.

5. Consistency and standardization

The most existing rules of the road were developed over the last 100 years. They are based on the assumption that drivers are human beings. But things are going to change. Consider this: how does law enforcement pull over a fully autonomous vehicle that has committed a traffic violation? Law enforcement and other agencies, therefore, must refine the legal mechanisms around the driver as the central actor in driving.

Another area where standardization is required is parking. The local governments must investigate policies and programs governing the location, form, price, and parking amount. This includes monitoring how changes in vehicle ownership models and CAV adoption could impact parking revenue, mainly for municipalities that rely heavily on this revenue to support public services. Local governments may also need to develop specifications for parking design for self-driving cars and may need to examine redevelopment opportunities for parking lots in dense urban areas.

6. Data capture and sharing

In the longer term, there is an expectation that CAVs will collect and share data as road and traffic conditions change. For instance, a lead vehicle, encountering congestion, major traffic events, hazard warnings, environmental conditions or road closures can share this information in real-time with other vehicles or corresponding agency, so subsequent vehicles can adjust accordingly. A road database inventory or a high-definition mapping is one of the most fundamental elements and will require highly detailed information such as lane geometry, horizontal and vertical curve characteristics, and speed limits. However, the cost of creating such high-definition maps is challenging. If an agency decides to invest in high-definition mapping, some guidance may be needed to address liability issues.

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