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The First Self-Driving Tesla Car

The First Self-Driving Tesla Car

The dream of self-driving cars has captured the public imagination for decades. As autonomous vehicles advance from science fiction to emerging reality, perhaps no company has done more to fuel excitement and speculation around this futuristic technology than Tesla. The electric car maker’s CEO Elon Musk has been one of the foremost proponents for a future where cars can drive themselves, fervently expressing his vision that fully autonomous vehicles are just around the corner.


As a pioneer in bringing self-driving capabilities to the mass market, Tesla’s Autopilot driver assistance features and more advanced Full Self-Driving (FSD) system represent ambitious strides towards realizing Musk’s vision. Since first releasing Autopilot in 2015, Tesla’s self-driving capabilities have rapidly evolved thanks to billions of miles of real-world driving data. Yet despite bold claims about achieving full autonomy, Tesla’s FSD still faces major limitations and relies on constant human oversight in its current form.


In this article, we will look back on the origins and developmental timeline of Tesla’s self-driving vehicle technology, from the early days of Autopilot to the ongoing evolution of Full Self-Driving. Tracing the history of Tesla’s pursuit of autonomy provides perspective on the hype versus reality around its driverless aspirations, and insight into the progress and challenges that lie ahead for true driverless cars.



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Early Development

Tesla began developing the foundational elements of its self-driving technology as early as 2013, shortly after the launch of the Model S sedan. At the time, Tesla was focused on delivering a handful of “driver assist” capabilities that formed the building blocks of autonomous driving. This included features like traffic-aware cruise control that could automatically adjust vehicle speed based on surrounding traffic, as well as auto steering that could keep the car centered within its driving lane. Tesla also developed an auto parking system that leveraged sensors and software to enable automatic parallel and perpendicular parking maneuvers.

While limited in scope compared to full autonomy, these initial Autopilot capabilities showcased Tesla’s early innovation in bringing semi-autonomous functionalities to market. The capabilities were made possible by Tesla’s uniquely advanced sensor suite and neural network technologies. Even in the early days, Tesla was collecting massive amounts of driving data to train and improve its self-driving algorithms.

 

Launch of Autopilot

In October 2015, Tesla took a major step towards full autonomy by launching its first version of Autopilot with the software update to version 7.0. This initial release of Autopilot introduced semi-autonomous driving capabilities to Tesla vehicles for the first time, enabled by a combination of cameras, radars, and sensors on the car.

With Autopilot activated, Teslas could automatically steer within a lane, change lanes with a simple tap of the turn signal, manage speed using traffic-aware cruise control, and even automatically park at the push of a button. This allowed drivers on highways and well-marked roads to take their hands off the wheel for extended periods of time, while Autopilot handled much of the driving.

However, the first generation of Autopilot faced criticism for relying primarily on cameras to perceive the driving environment, rather than utilizing a suite of redundant sensors like lidar as other self-driving companies were doing. This camera-focused approach raised concerns about limitations in poor visibility conditions. Nonetheless, the launch of Autopilot marked the start of Tesla’s foray into true self-driving capabilities, setting the stage for more advanced systems to come.

 

Collecting Data

A critical component of developing effective self-driving technology is amassing substantial real-world driving data. By 2016, Tesla began aggregating billions of miles of data from its growing fleet of Autopilot-equipped vehicles out on the roads. This provided the company with an immense bank of driving data to feed into the neural network behind Autopilot, helping significantly improve its capabilities through deep learning.

Having a massive fleet of customer cars equipped with sensor suites out in the wild allowed Tesla to passively collect terabytes upon terabytes of image, video, and sensor data of real-world driving scenarios. This proved invaluable for training and enhancing the sophistication of the AI “brain” powering Autopilot and its progression towards full autonomy. Tesla understood early on that robust data collection from its vehicles was the key to unlocking next-level self-driving functionality.

While other autonomous vehicle companies had to rely on limited data from test mules and smaller research fleets, Tesla had hundreds of thousands of cars transmitting back billions of miles of driving data every day. This data advantage helped Tesla rapidly iterate and enhance its neural network, inching closer to delivering on the promise of true full self-driving capability powered by artificial intelligence. Tesla’s head start in real-world data continues to be one of its core strategic assets in the race to develop safe and reliable autonomous driving.

 

Moving Towards Full Self-Driving Capability

In 2016, Tesla revealed plans for an enhanced version of Autopilot called Enhanced Autopilot, as well as a new package called Full Self-Driving Capability. These promised even more advanced autonomous driving abilities beyond the original Autopilot’s simple highway lane keeping and traffic-aware cruise control.

Enhanced Autopilot added new features like automatic lane changing, Autopark for parallel and perpendicular parking, and Summon to automatically park and retrieve your Tesla from tight spaces. It could transition between highways and city streets more smoothly and included more advanced sensing capabilities.

The Full Self-Driving Capability package was Tesla’s next step towards fully autonomous operation. Though early versions still required constant human supervision, it aimed to eventually enable automatic driving from highway on-ramps to off-ramps, including interchanges and making lane changes. Musk envisioned it would turn a Tesla into a fully autonomous robotaxi.

While not yet capable of full autonomy, Enhanced Autopilot and the early FSD releases did represent meaningful progress for Tesla’s self-driving programs. The company was clearly thinking ahead to a future of truly driverless transportation, even if the technology wasn’t quite ready for it yet.

 

Hardware 2 Vehicles

In October 2016, Tesla began producing vehicles equipped with new self-driving hardware, referred to as Hardware 2. These vehicles included more cameras, radar, and computing power compared to earlier models. The additional sensors and compute enabled Tesla to collect richer data and make progress towards full self-driving capabilities.

The Hardware 2 upgrade was first introduced on all Tesla vehicles produced from October 2016 onward. It also enabled existing Tesla owners to purchase a retrofit of the new hardware. This allowed Tesla to upgrade its fleet with autonomous-ready vehicles.

Despite the name “Hardware 2”, these vehicles were still not fully autonomous. They contained the necessary cameras, radar, ultrasonic sensors and onboard computer needed for full self-driving features. However, the software was not yet ready for unsupervised autonomous operation. Human drivers still needed to remain alert and ready to takeover at all times when using Autopilot or any self-driving features.

The introduction of Hardware 2 vehicles was an important milestone for Tesla’s self-driving program. With an autonomous-ready fleet, Tesla could now focus on software development and data collection to inch closer to true full autonomy while having the necessary sensor and compute hardware already in place.

 

Full Self-Driving Computers

In 2019, Tesla began equipping older vehicles with newly designed, custom self-driving computers to provide the additional computing power required for more advanced autonomous capabilities. These purpose-built modules were engineered in-house by Tesla to accelerate neural network training and enable full self-driving functionality. Known as Hardware 3, the new computer was first produced in 2019 and began being retrofitted into older Model S and Model X vehicles that same year.

The new computer provided approximately 21 times more performance than previous generations and was specifically optimized for the heavy parallel processing needs of advanced AI neural networks. This gave Tesla vehicles the necessary hardware capabilities to start progressing towards true full self-driving as the software continued to be refined. With greater computing muscle, the vehicles could process more complex scenarios and make quicker driving decisions using the rapidly improving neural networks.

By upgrading older vehicles with the latest self-driving hardware, Tesla ensured its entire fleet would eventually have the power to utilize full autonomous features as the software matured. The retrofits began slowly in 2019 but ramped up through 2020 and 2021. Today, the vast majority of Tesla vehicles on the road are equipped with the Full Self-Driving computers, providing the foundation for Tesla’s autonomous ambitions as development marches steadily forward.

 

FSD Beta Begins

In late 2020, Tesla took the next major step towards full self-driving capability by beginning early limited access to an FSD beta software release. This marked the start of a wider rollout of more advanced autonomous features to select Tesla owners. However, the initial FSD beta still required constant human supervision, despite its name. Musk warned that drivers needed to remain fully attentive and ready to take control at any time. This was not a hands-free autonomous system yet. Tesla started slow and selective with the FSD beta, only granting access to around 1,000 early testers at first. The early testers were able to try out the system in a controlled environment and provide important feedback to Tesla.

 

Wider FSD Beta Release

In October 2021, Tesla opened up its FSD beta software to a wider group of users through an over-the-air update. After initially restricting access to a small pool of expert and careful drivers, Tesla decided to expand the beta program to include more regular customers. This enabled the company to receive broader feedback and data on FSD performance in a variety of everyday driving scenarios.

Since the wider release, over 100,000 Tesla owners have gotten access to test the latest FSD beta builds. This has provided Tesla with billions more miles of real-world driving data to help train and refine its neural network that powers autonomous driving capabilities. Having a larger test group has accelerated learnings for Tesla’s AI team, though it has also led to more scrutiny as inexperienced drivers trial the imperfect system.

The wider FSD beta testing base has given Tesla an advantage over competitors when it comes to autonomous driving development. The average customer is able to experience capabilities like automatic lane changes, smart navigation, and more using just the vehicle’s cameras and on-board computer. However, the beta label remains as human oversight is still required given the unfinished state of the technology. As testing continues, Tesla aims to eventually validate FSD to remove the beta status and enable true autonomous operation.

 

FSD Capabilities

The current FSD beta software has come a long way in handling complex urban driving situations that previously challenged Tesla’s self-driving abilities. With the latest FSD versions, Teslas can now navigate residential streets, make unprotected left turns, and handle traffic lights and stop signs. However, while impressive, Tesla still cautions that the system requires constant and careful monitoring by an attentive human driver. FSD beta enables the car to steer, accelerate, brake and make lane changes automatically in many circumstances, but true autonomous capability has not yet been achieved.

Features like Autopark for parallel and perpendicular parking ease certain maneuvers, and Tesla’s Neural Network continues learning from real-world driving data. But for now, FSD is considered a driver assistance system despite its name, not allowing the driver to be completely hands-free. With the system making judgment calls in complex unpredictable scenarios, human oversight remains critical. FSD beta testers are warned to keep their eyes on the road and be prepared to take control at all times.

While Musk originally envisioned full self-driving by 2018, the technology has proven more challenging to perfect than anticipated. FSD beta has come a long way since its early “Mad Max mode” days, but still drives more like a newly-licensed teenager than a seasoned autonomous robot. As the capabilities evolve, Musk continues to tout the life-saving potential of autonomous technology while emphasizing that FSD is still in early development. While true self-driving cars remain on the distant horizon, Tesla’s incremental progress shows promising steps in that direction.

 

The Data Advantage

One of Tesla’s key advantages in developing self-driving technology is the vast amounts of real-world driving data they are able to collect from their customers’ vehicles. As the first automaker to deploy an advanced driver assist system at scale, Tesla has amassed billions of miles of driving data to help train and improve the neural network that powers Autopilot and Full Self-Driving.

This real-world data is incredibly valuable for developing robust self-driving systems. While other companies have to rely more on simulation, Tesla’s fleet acts as a data collection system, sending back camera footage, sensor readings, and other telemetry data from actual on-road driving. According to Tesla, their vehicles provide 1 billion miles of data every 10 hours.

All of this data allows Tesla to continuously enhance its self-driving algorithms. The more varied driving data the system is exposed to, the more capabilities it can take on, from detecting stop signs and traffic lights to navigating complex urban environments. No competitor comes close to Tesla’s data set – for example, Waymo has logged just over 20 million self-driven miles in total. Tesla’s ability to leverage billions of miles of data gives it a significant leg up in evolving its AI driver.

As Tesla vehicles continue clocking more autonomous miles every day, the neural network keeps getting smarter. This real-world experience is invaluable in taking Tesla closer to true full self-driving capability, even as the system still requires human oversight for now. But the promise is that one day, thanks to this wealth of data, Tesla owners may be able to take their hands off the wheel for good.

 

Regulatory Barriers

While Tesla’s self-driving technology continues to improve, regulations have not kept pace with the rapid advances. Tesla faces significant hurdles to enable true driverless operation without human oversight due to regulatory restrictions.

Current laws in most jurisdictions require a human driver to remain alert and ready to take control, even when using advanced driver assist systems like Autopilot. Tesla warns that their Full Self-Driving software still necessitates an attentive driver prepared to intervene at any time. Removing the human driver fully from the loop is not yet legally permitted.

Self-driving vehicles face a patchwork of state-level regulations, with no consistent federal framework in the US. Some states have more restrictive rules than others regarding testing and deploying autonomous vehicles. Government approval processes for certifying fully driverless systems remain lengthy and complex.

While Tesla amasses driving data at scale, regulators caution against releasing unreliable technology on public roads too soon. Striking the right balance between safety and innovation will be key. Critics argue lax regulations have allowed Tesla to use untrained drivers to test FSD beta on open roads.

As Musk aims for a future where human oversight is unnecessary, Tesla must continue engaging with policymakers. Navigating complex legal hurdles will likely slow the path towards unrestricted autonomous driving. But Musk remains intent on proving the capabilities of Tesla’s self-driving vehicles, while lobbying for updated regulations to enable full autonomy in the future.

 

The Road Ahead

While still a work in progress requiring watchful human drivers, Tesla’s ever-evolving Autopilot and Full Self-Driving programs have already made great strides in advancing self-driving vehicle tech. With Tesla leading the way in real-world testing data collection and AI development, we may one day truly see fully autonomous cars thanks to the groundwork Musk laid when he first envisioned the possibility. But for now, Tesla’s self-driving journey continues as an ambitious vision still on the road to completion.

 

Conclusion

As we have seen, Tesla’s journey towards full self-driving capability has been an ongoing process spanning over a decade. While still requiring attentive human monitoring, the incremental advancements made to Autopilot and the introduction of the FSD beta have been pivotal steps in the pursuit of autonomous vehicles. Tesla’s billions of miles of real-world driving data give it a competitive edge in training the AI neural networks that will one day enable full autonomy. However, Elon Musk’s optimistic timelines have proven overly ambitious, and significant regulatory and technological hurdles remain.

Looking ahead, we can be cautiously hopeful that Tesla’s persistence in iterating on its self-driving software while collecting invaluable data will continue to inch the technology towards maturity. But the final leap to 100% autonomous operation without human oversight remains elusive. As Musk himself has acknowledged, solving full self-driving is a very hard engineering challenge and “generalized self-driving is a hard problem, as it requires solving a large part of real-world AI.”

While the road to completion is long, Tesla has made undeniable contributions in advancing self-driving vehicle technology. By spearheading the mass adoption of ADAS features like Autopilot, Tesla paved the way for consumer awareness and acceptance of autonomous systems. As the company remains laser-focused on progressing towards full autonomy, they are pushing the entire industry towards that once-distant vision. Tesla’s leadership in self-driving innovation ensures they will leave a lasting impact, regardless of when fully driverless cars become a reality.

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Questions About The First Self-Driving Tesla Car

Tesla’s first car with self-driving capability available in Canada was the Model S, which debuted in 2015. The Model S came equipped with Autopilot, Tesla’s driver assistance system that enabled semi-autonomous driving. Autopilot provided features like automatic steering, lane changing, self-parking, and automatic braking. At launch, Autopilot had limited functionality in Canada due to regulations, but its capabilities have expanded over time via over-the-air software updates.



Tesla’s Full Self Driving (FSD) beta first became available in Canada in March 2022. The FSD beta enables Tesla vehicles to automatically drive on city streets utilizing the vehicle’s cameras and sensors for navigation and object detection. The software remains in beta testing in Canada with a limited number of users as Tesla continues to develop and validate its self-driving capabilities.

The first Model S vehicles sold in Canada in 2013 came equipped with radar-based safety features like adaptive cruise control, automatic emergency braking, blind spot monitoring, and lane departure warning. However, these were driver assistance features and did not provide any self-driving capabilities. Autopilot’s semi-autonomous driving functionality arrived in 2015 via an over-the-air software update to Model S vehicles with the required hardware. This enabled hands-free driving on highways in Canada, subject to the system’s limitations at that time.

No, Tesla’s FSD software remains in beta testing in Canada and is not approved for completely driverless operation. Canadian regulations require a fully licensed driver behind the wheel of self-driving test vehicles, ready to take control at any time. FSD enables automatic driving in many situations but still requires an attentive driver monitoring the system. True autonomous driving without a driver is not yet legal in Canada.



Currently, Ontario, Quebec, Alberta and British Columbia permit testing of automated driving systems on public roads if companies obtain permits from the provincial transport authorities. Ontario was one of the first provinces to allow on-road testing of self-driving vehicles. Quebec, Alberta and B.C have also updated regulations over the past few years to enable autonomous vehicle pilot projects on public highways or in designated test sites.



Canadian regulations around self-driving vehicle testing are more stringent than the U.S. due to additional oversight and reporting requirements. For example, companies must submit detailed safety plans, have licensed drivers behind the wheel, and report any collisions or near misses. Canada also does not allow completely driverless testing without human supervisors as some U.S. states do. On the other hand, the decentralized Canadian system allows provinces to move faster in approving self-driving car pilots and testing compared to the overall U.S. approach.

The snowy winters and variable weather in much of Canada can be very difficult for self-driving sensors and software to handle reliably. Heavy snowfall creates vision obstruction issues while snow-covered roads hide lane markings and road edges. Icy conditions also make traction and maneuvering difficult, especially when mixed precipitation types occur. Canada’s long highways with limited data connectivity pose another challenge for real-world validation. Overall, Canada offers a wide range of extreme driving scenarios to rigorously test autonomous vehicles.



Most other companies testing AVs in Canada focus on specialized self-driving shuttles or delivery robots operating at low speeds. In contrast, Tesla’s Autopilot and FSD software aim to handle full-speed highway driving and urban navigation, representing a more advanced capability level to validate. That said, Tesla’s consumer vehicle approach has more variability compared to highly mapped shuttles. Waymo, GM’s Cruise and Uber Advanced Technologies Group have also conducted more limited testing of automated passenger vehicles in Canada.



There have not been any known major collisions reported involving Teslas in FSD mode in Canada so far. However, Tesla’s beta testing approach makes their overall safety record difficult to analyze. Minor scrapes, close calls and software glitches are likely happening but mostly go unreported in the early stages of validation. Canada has strict crash disclosure rules for companies testing AVs so any serious incidents with Tesla’s system would likely become public knowledge and raise scrutiny of their technology.

The Greater Toronto Area, Vancouver, Montreal, Calgary and Ottawa are emerging as hub locations in Canada for self-driving vehicle testing. All five cities offer diverse and challenging driving environments, plus technology talent and infrastructure assets needed to support autonomous vehicle R&D. Specific testing sites also exist, like Ontario’s AVIN hub, the Montreal-Toronto corridor AV track, and Vancouver’s Surrey test circuit. As regulations progress, other mid-sized Canadian cities will also become viable validation spots.

Most experts estimate it will be 5-10 years before self-driving cars without human oversight can service the public in Canada in limited applications, such as within geo-fenced areas or specific weather conditions. However, a human driver ready to take control will still be needed in most situations for many years to come. Full autonomy in all road conditions could take until 2040 or later before being reliably achievable from both technology and regulatory perspectives in Canada.

The winter climate in Canada can interfere with the operation of the cameras, ultrasonic sensors and radar used by Tesla’s Autopilot driver assistance platform. Snow, ice and heavy rain can obstruct sensors while salt and grime buildup affects calibration. Cold temperatures can also degrade sensor performance. Tesla has made improvements in this area with camera defrosting and advanced neural net training on winter data but detection reliability remains a challenge.



Some leading Canada-based autonomous vehicle technology companies viewed as competitors to Tesla include Waabi Innovations, DeepSense.ai, AB Dynamics, Edge Case Research, and Renesas Electronics. Canada also has an emerging ecosystem of AI and computer vision startups advancing self-driving software, such as DarwinAI, Acerta Analytics, Intellivision Technologies and Metamoto. While smaller, many have extensive talent and innovative approaches to complement automakers like Tesla.



As one of the first regions where Tesla launched, Canada represents a valuable real-world testing ground, with thousands of Tesla vehicles providing continuous data to enhance Autopilot and Full Self-Driving capabilities. Tesla’s Canadian fleet size is estimated at over 100,000 vehicles and counting. Even assuming only a fraction currently have FSD activated, that still translates to petabytes of image, sensor and navigation data being amassed across various Canadian road types, weather conditions and driving scenarios.

Transport Canada is Canada’s federal regulator responsible for motor vehicle safety and standards. Recent initiatives indicate preparations for advanced autonomous vehicles are ramping up. For example, TC published federal AV testing guidelines in 2020. They also advocate for international vehicle standards via the UN WP.29 group. Additionally, TC is collaborating with US regulators to harmonize technical approaches for certifying self-driving systems as safe for public operation. Mandatory reporting of testing activities also informs policy.



Yes, Tesla owners in Canada could encounter insurance problems if operating their vehicles using Autopilot or the FSD beta on public roads. Some Canadian providers refuse to insure vehicles with certain self-driving features enabled or may exclude coverage should a collision occur while they are activated. Drivers need to closely review their auto insurance policies before turning on advanced driver assistance features. Using FSD beta may be considered violation of insurance terms entirely due to its experimental nature.



Canadians test-driving Teslas with semi-autonomous features like Autopilot or FSD beta must remain vigilant and ready to instantly take control of the vehicle. Hands must stay on the wheel and eyes on the road at all times to monitor the driving environment. It’s also smart to first test self-driving only in simple conditions before progressing to more complex situations. Understanding feature limitations and disabling when in doubt are other tips. Responsible testing and reporting of issues will help advance safety.

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