Tesla Autopilot

Tesla Autopilot, later marketed as Enhanced Autopilot after a second hardware version started to be shipped, is an advanced driver-assistance system feature offered by Tesla that has lane centeringadaptive cruise control, self-parking, ability to automatically change lanes without requiring driver steering, and enables the car to be summoned to and from a garage or parking spot. Planned improvements to Enhanced Autopilot include transitioning from one freeway to another and exiting the freeway when your destination is near.

As an upgrade above and beyond Enhanced Autopilot’s capabilities, the company’s stated intent is to offer full self-driving at a future time, acknowledging that legal, regulatory, and technical hurdles must be overcome to achieve this goal.[2] As of February 2018, Tesla indicates that a demonstration of a self-driving coast to coast drive will be ready in three to six months.[3]

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Autopilot was first offered on October 9, 2014, for Tesla Model S, followed by the Model X upon its release.[4] Autopilot was included within a “Tech Package” option. At that time Autopilot features included semi-autonomous drive and parking capabilities.[5][6][7] Initial versions of Autopilot were developed in partnership with the Israeli company Mobileye.[8]Tesla and Mobileye ended their partnership in July 2016.[9][10] In October 2015, Tesla released Autopilot version 7.0 to its customers.[11] In December 2015, Tesla announced that it will remove some self-driving features to discourage customers from engaging in risky behaviour. Autopilot Firmware 7.1 made those changes and includes remote parking technology known as Summon that can park and can bring the car to the driver without the driver in the car.[12][13][14]

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On August 31, 2016, Elon Musk announced Autopilot 8.0, that processes radar signals to create a coarse point cloud similar to Lidar to help navigate in low visibility conditions, and even to ‘see’ in front of the car ahead.[15][16] Autopilot, as of version 8, uses radar as the primary sensor instead of the camera.[17] In November 2016, Autopilot 8.0 was updated to have a more noticeable signal to the driver that it is engaged and it requires drivers to touch the steering wheel more frequently, otherwise Autopilot will turn off.[18][19] By November 2016, Autopilot had operated actively on hardware version 1 vehicles for 300 million miles (500 million km) and 1.3 billion miles (2 billion km) in shadow mode.[20]

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As of October 2016, Tesla said all vehicles come with the necessary sensing and computing hardware, known as Hardware version 2 (HW2), for future fully autonomous operation (SAE Level 5), with software being made available as it matures.[21] The company offers various free/extra-cost options for enabling Autopilot-associated features/services. Autopilot on hardware version 1 cars is available for US$2,500 ($3,000 after delivery). For HW2 cars, Autopilot is available as “Enhanced Autopilot” for $5,000 ($6,000 after delivery) and future full self-driving capability is an additional $3,000 ($4,000 after delivery).[22]

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The first release of Autopilot for HW2 cars was in February 2017. It included adaptive cruise control, autosteer that was enabled on divided highways, autosteer on ‘local roads’ up to a speed of 35 mph or a specified number of mph over the local speed limit.[23] Firmware version 8.1 for HW2 began in June 2017 that has many new features including a new Autopilot driving-assist algorithm, full-speed braking and handling parallel and perpendicular parking.[24]

On April 28, 2017, Elon Musk predicted that in around two years drivers would be able to sleep in their Tesla until it finishes the trip.[25] In the middle of 2017, Tesla planned to demonstrate full self-driving by the end of 2017.[26][27] In February 2018, Tesla indicates the demonstration of a self-driving coast to coast drive will be ready in three to six months.[3]


Hardware 1

Vehicles manufactured after late September 2014 are equipped with a camera mounted at the top of the windshield, forward looking radar (supplied by Bosch)[28][29] in the lower grille and ultrasonic acoustic location sensors in the front and rear bumpers that provide a 360-degree view around the car. The computer is the Mobileye EyeQ3.[30] This equipment allows Model S to detect road signs, lane markings, obstacles, and other vehicles. Upgrading from Hardware 1 to Hardware 2 is not offered as it would require substantial work and cost.[31]

Hardware 2

Hardware 2, included in all vehicles manufactured after October 2016, includes an Nvidia Drive PX 2[32] GPU for CUDA based GPGPU computation.[33][34] Tesla claimed that Hardware 2 provided the necessary equipment to allow full self-driving capability at SAE Level 5. The hardware includes 8 surround cameras and 12 ultrasonic sensors, in addition to the forward-facing radar with enhanced processing capabilities.[21] The Autopilot computer is replaceable to allow for future upgrades.[35] The radar is claimed to be able to observe beneath and ahead of the vehicle in front of the Tesla; the radar can see vehicles through heavy rain, fog or dust.[36]


Hardware 2.5

An updated Hardware version referred to as ‘HW 2.5’ (also known as ‘2.1’) was released in July 2017, with cars built from August 2017 containing the updated hardware set. In HW 2.5 there is a secondary node (without a GPU) to provide more computing power and wiring redundancy which is to slightly improve reliability.[53][54]

Driving features

Tesla requires operators to monitor the vehicle at all times, just as the Federal Aviation Administration requires pilots to monitor aircraft on autopilot. Autopilot includes multiple capabilities, including adaptive cruise control and lane departure warning.

Software updates

Autopilot-enabled cars receive Autopilot software updates wirelessly, the same as all other Tesla software updates.

Adaptive cruise control

Autopilot has the ability to follow another car, maintaining a safe distance from it as it speeds up and slows down. It can observe a second vehicle in front of the vehicle that it is following. It also slows on tight curves and when a car crosses the road in front of it. It can be enabled at any speed above 17 mph. By default, it sets the limit at the current speed limit plus/minus any driver-specified offset.


Autopilot alerts the driver under various circumstances, such as a surprising situation on the road or excessive inattention by the driver. If the driver dismisses three audio warnings within an hour, Autopilot is disabled until the car is parked. This is to prevent experienced drivers from excessive reliance on built-in safety features. At speeds under 8 mph on divided highways, Autopilot functions indefinitely without the driver’s hands on the wheel. Under 45 mph free hands are allowed for five minutes, unless the car detects lateral acceleration. Above 45 mph free hands are allowed for three minutes if following another vehicle or one minute without following a car.[36]


Autopark drives the car into a parking spot, while Summon drives it out. Configuration settings control maximum distance, side clearance and bumper clearance. This feature activates Homelink to open and close garage doors and it is available using the fob or the Tesla mobile app.[55] As of March 2017, Summon was available in “beta” for HW2. Controls include bumper, side clearance and summon distance.[56]


Autosteer steers the car to remain in whatever lane it is in (known as lane-keeping). With HW1, it is also able to safely change lanes as directed by a tap of the turn signal.[57] As of May 2017, HW2 is limited to 90 mph (145 km/h) on highway roads and the former 35 mph (56 km/h) speed limit on non-highway roads was removed, instead limiting to five over the speed limit or 45 mph (72 km/h) if no speed limit is detected.[58]

Safety features

The Autopilot can detect a potential front or side collision with another vehicle, bicycle or pedestrian within a distance of 525 feet (160 m), if one is found it sounds a warning.[59]Autopilot has automatic emergency braking that detects objects that may hit the car and applies the brakes. Autopilot also can automatically adjust the high/low beam headlights as the nighttime lighting changes.

Speed assist

Front-facing cameras detect speed limit signs and display the current limit on the dashboard or center display. Limits are compared against GPS data if no signs are present.[59]

Public debate

Some industry experts have raised questions about the legal status of autonomous driving in the U.S. and whether Tesla owners would violate current state regulations when using the Autopilot function. The few states that have passed laws allowing autonomous cars on the road, limit their use for testing purposes; not for use by the general public. Also, there are questions about the liability for autonomous cars in case there is a mistake.[60] A Tesla spokesman said there is “nothing in our autopilot system that is in conflict with current regulations.” “We are not getting rid of the pilot. This is about releasing the driver from tedious tasks so they can focus and provide better input.” Google‘s director of self-driving cars said he does not think there is a regulatory block as long as the self-driving vehicle met crash-test and other safety standards. A spokesman for the U.S. National Highway Traffic Safety Administration (NHTSA) said that “any autonomous vehicle would need to meet applicable federal motor vehicle safety standards” and the NHTSA “will have the appropriate policies and regulations in place to ensure the safety of this type of vehicles.”[60]

According to Elon Musk, “We really designed the Model S to be a very sophisticated computer on wheels. Tesla is a software company as much as it is a hardware company. A huge part of what Tesla is, is a Silicon Valley software company. We view this the same as updating your phone or your laptop.”[61] Full autonomy is “really a software limitation: The hardware exists to create full autonomy, so it’s really about developing advanced, narrow AI for the car to operate on.“[62][63]

The Autopilot development focus is on “increasingly sophisticated neural nets that can operate in reasonably sized computers in the car”.[62][63] According to Musk, “the car will learn over time“, including from other cars.[64] Early data after 47 million miles of driving in Autopilot mode shows the probability of an accident is at least 50% lower when using Autopilot.[65] However, Ars Technica notes that the brake system tends to initiate later than some drivers expect.[66] One driver claimed that Tesla’s Autopilot failed to brake, resulting in collisions. Tesla pointed out that the driver deactivated the cruise control of the car prior to the crash.[67] Ars Technica also notes that the lane changes are semi-automatic; the driver must activate the turn signal in order for the car to initiate a lane change.[68]

Tesla’s Autopilot with Hardware version 1 (HW1) can be classified as somewhere between levels 2 and 3 under the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA) five levels of vehicle automation. At this level, the car can act autonomously but requires the driver to be prepared to take control at a moment’s notice.[69][70] HW1 is suitable only on limited-access highways, and sometimes will fail to detect lane markings and disengage itself. In urban driving the system will not read traffic signals or obey stop signs. This system also does not detect pedestrians or cyclists,[71] and while AP1 detects motorcycles,[72] there has been two instances of AP rear-endingmotorcycles.[73]

There has been significant controversy over the media response to the fatal Tesla accident described in the below section. Whilst a significant amount of blame was apportioned to Tesla for the failure of its Autopilot system, it must be noted that the system at the time of the accident was in a beta phase and not ready for widespread public use, and also required the driver to ensure that their hands remained on the steering wheel at all times, and to be prepared to resume manual driving at any moment.[74][undue weight? ]Hence, when used as an assistive feature (as intended by Tesla), some hold the view that Autopilot can only enhance road safety,[74] assuming it does not lull the driver into complacent inattention.

Autopilot potentially saved the life of a pedestrian in Washington, D.C. on the night of July 17, 2016,[75][76] and played a pivotal role in a medical emergency involving 37-year-old Joshua Neally that same month.[77] Neally was driving his Tesla Model X when he suffered a pulmonary embolism that caused intense panic and rendered him incapable of driving.[78] Neally used Autopilot to drive most of the highway to a local hospital. At the off-ramp, Neally took control of the car and drove to the emergency room.[78]

Legal Challenges

Tesla’s Autopilot is facing a class action suit that claims the second-generation Enhanced Autopilot system is “dangerously defective.”[79]

Serious crashes

Handan, China (January 20, 2016)

On January 20, 2016, the driver of a Tesla Model S in Handan, China was killed when their car crashed into a stationary truck.[80] The Tesla was following a car in the far left lane of a multi-lane highway; the car in front moved to the right lane to avoid a truck stopped on the left shoulder, and the Tesla, which the driver’s father believes was in Autopilot mode, did not slow before colliding with the stopped truck.[81] According to footage captured by a dashboard camera, the stationary street sweeper on the left side of the expressway partially extended into the far left lane, and the driver did not appear to respond to the unexpected obstacle.[82]

In September 2016, the media reported the driver’s family had filed a lawsuit in July against the Tesla dealer who sold the car.[83] The family’s lawyer stated the suit was intended “to let the public know that self-driving technology has some defects. We are hoping Tesla, when marketing its products, will be more cautious. Don’t just use self-driving as a selling point for young people.”[81] Tesla released a statement which said they “have no way of knowing whether or not Autopilot was engaged at the time of the crash” since the car telemetry could not be retrieved remotely due to damage caused by the crash.[81] Telemetry was recorded locally to a SD card and given to Tesla, who decoded it and provided that data to a third party for independent review. Tesla added that “while the third-party appraisal is not yet complete, we have no reason to believe that Autopilot on this vehicle ever functioned other than as designed.”[84]

Williston, Florida (May 7, 2016)

The first known fatal accident involving a Tesla engaged in Autopilot mode took place in Williston, Florida, on May 7, 2016. The driver was killed in a crash with a 18-wheel tractor-trailer.

By late June 2016, the U.S. National Highway Traffic Safety Administration (NHTSA) opened a formal investigation into the accident, working with the Florida Highway Patrol. According to the NHTSA, preliminary reports indicate the crash occurred when the tractor-trailer made a left turn in front of the Tesla at an intersection on a non-controlled access highway, and the car failed to apply the brakes. The car continued to travel after passing under the truck’s trailer.[85][86][87] The diagnostic log of the Tesla indicated it was traveling at a speed of 74 mi/h (119 km/h) when it collided with and traveled under the trailer, which was not equipped with a side underrun protection system.[88]:12 The underride collision sheared off the Tesla’s glasshouse, destroying everything above the beltline, and caused fatal injuries to the driver.[88]:6–7; 13 Approximately nine seconds after colliding with the trailer, the Tesla traveled another 886.5 feet (270.2 m) and came to rest after colliding with two chain-link fences and a utility pole.[88]:7; 12

The NHTSA’s preliminary evaluation was opened to examine the design and performance of any automated driving systems in use at the time of the crash, which involves a population of an estimated 25,000 Model S cars.[89] On July 8, 2016, the NHTSA requested Tesla Inc. to hand over to the agency detailed information about the design, operation and testing of its Autopilot technology. The agency also requested details of all design changes and updates to Autopilot since its introduction, and Tesla’s planned updates scheduled for the next four months.[90]

According to Tesla, “neither autopilot nor the driver noticed the white side of the tractor-trailer against a brightly lit sky, so the brake was not applied.” The car attempted to drive full speed under the trailer, “with the bottom of the trailer impacting the windshield of the Model S.” Tesla also stated that this was Tesla’s first known Autopilot-related death in over 130 million miles (208 million km) driven by its customers while Autopilot was activated. According to Tesla, there is a fatality every 94 million miles (150 million km) among all type of vehicles in the U.S.[85][86][91] It is estimated that billions of miles will need to be traveled before Tesla Autopilot can claim to be safer than humans with statistical significance (although fewer than billions of miles will be needed if Tesla Autopilot is more dangerous). Researchers say that Tesla and others need to release more data on the limitations and performance of automated driving systems if self-driving cars are to become safe and understood enough for mass market use.[92][93]

The truck’s driver told the Associated Press that he could hear a Harry Potter movie playing in the crashed car, and said the car was driving so quickly that “he went so fast through my trailer I didn’t see him.” “It was still playing when he died and snapped a telephone pole a quarter mile down the road.” According to the Florida Highway Patrol, they found in the wreckage an aftermarket portable DVD player. It is not possible to watch videos on the Model S touchscreen display.[87][94] A laptop computer was recovered during the post-crash examination of the wreck, along with an adjustable vehicle laptop mount attached to the front passenger’s seat frame. The NHTSA concluded the laptop was probably mounted and the driver may have been distracted at the time of the crash.[88]:17–19; 21

Dr. Deb Bruce, head of the investigation team, announces results to the Board on September 12, 2017. In July 2016, the U.S. National Transportation Safety Board (NTSB) announced it had opened a formal investigation into the fatal accident while Autopilot was engaged. The NTSB is an investigative body that only has the power to make policy recommendations. An agency spokesman said, “It’s worth taking a look and seeing what we can learn from that event so that as that automation is more widely introduced we can do it in the safest way possible.” The NTSB opens annually about 25 to 30 highway investigations.[95] In September 2017, the NTSB released its report, determining that “the probable cause of the Williston, Florida, crash was the truck driver’s failure to yield the right of way to the car, combined with the car driver’s inattention due to overreliance on vehicle automation, which resulted in the car driver’s lack of reaction to the presence of the truck. Contributing to the car driver’s overreliance on the vehicle automation was its operational design, which permitted his prolonged disengagement from the driving task and his use of the automation in ways inconsistent with guidance and warnings from the manufacturer.”[96].

In January 2017, the NHTSA Office of Defects Investigations (ODI) released a preliminary evaluation, finding that the driver in the crash had seven seconds to see the truck and identifying no defects in the Autopilot system; the ODI also found that the Tesla car crash rate dropped by 40 percent after Autosteer installation,[97][98] but later also clarified that it did not assess the effectiveness of this technology or whether it was engaged in its crash rate comparison.[99] The NHTSA Special Crash Investigation team published its report in January 2018.[88] According to the report, for the drive leading up to the crash, the driver engaged Autopilot for 37 minutes and 26 seconds, and the system provided 13 “hands not detected” alerts, to which the driver responded after an average delay of 16 seconds.[88]:24 The report concluded “Regardless of the operational status of the Tesla’s ADAS technologies, the driver was still responsible for maintaining ultimate control of the vehicle. All evidence and data gathered concluded that the driver neglected to maintain complete control of the Tesla leading up to the crash.”[88]:25

Culver City, California (January 22, 2018)

On January 22, 2018, a Tesla Model S crashed into a fire truck parked on the side of the I-405 freeway in Culver City, California while travelling at a speed exceeding 50 mph (80 km/h) and the driver survived.[100] The driver said he was using Autopilot, according to the Culver City Fire Department, which reported the crash over Twitter at approximately 8:30 A.M. The fire truck and a California Highway Patrol vehicle were parked in the left emergency lane and carpool lane of the southbound 405, blocking off the scene of an earlier accident, with emergency lights flashing.[101]

Autopilot may not detect stationary vehicles at highway speeds and it cannot detect some objects.[102] Other advanced driver-assistance systems have similar limitations. Raj Rajkumar, who studies autonomous driving systems at Carnegie Mellon University, believes the radars used for Autopilot are designed to detect moving objects but are “not very good in detecting stationary objects”.[103] Both NTSB and NHTSA have dispatched teams to investigate the crash.[104] Hod Lipson, director of Columbia University‘s Creative Machines Lab, faulted the diffusion of responsibility concept: “If you give the same response to two people, they each will feel safe to drop the ball. Nobody has to be 100%, and that’s a dangerous thing.”[105]

Mountain View, California (March 23, 2018)

On March 23, 2018, a second US Autopilot fatality occurred in Mountain View, California.[106] The crash occurred just before 9:30 A.M. on southbound US 101 at the carpool lane exit for southbound Highway 85, at a concrete barrier where the left-hand offramp separates from 101. After the Model X crashed into the narrow concrete barrier, it was struck again by two following vehicles, and then it caught on fire.[107]

Both the NHTSA and NTSB are investigating the March 2018 crash.[108] Another driver of a Model S demonstrated that Autopilot appeared to be confused by the road stripes in April 2018. The gore ahead of the barrier is marked by diverging solid white lines (a vee-shape); the Autosteer feature of the Model S appeared to mistakenly use the left-side white line instead of the right-side white line as the lane marking for the far left lane, which would have led the Model S into the same concrete barrier had the driver not taken control.[109] Ars Technica concluded, “that as Autopilot gets better, drivers could become increasingly complacent and pay less and less attention to the road.”[110]

In a corporate blog post, Tesla noted the impact attenuator separating the offramp from US 101 had been previously crushed and not replaced prior to the Model X crash on March 23.[106][111] The post also stated that Autopilot was engaged at the time of the crash, and the driver’s hands had not been detected manipulating the steering wheel for six seconds before the crash. Vehicle data showed the driver had five seconds and 150 metres (490 ft) “unobstructed view of the concrete divider, […] but the vehicle logs show that no action was taken.”[106] The NTSB investigation had been focused on the damaged impact attenuator and the vehicle fire after the collision, but after it was reported the driver had complained about the Autopilot functionality,[112] the NTSB announced it would also investigate “all aspects of this crash including the driver’s previous concerns about the autopilot.”[113] A NTSB spokesman stated the organization “is unhappy with the release of investigative information by Tesla”.[114] Elon Musk dismissed the criticism, tweeting that NTSB was “an advisory body” and that “Tesla releases critical crash data affecting public safety immediately & always will. To do otherwise would be unsafe.”[115]


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    16.   Anthony, Mike (2014-10-03). “UPDATE: Tesla Model S Now With Driver Assist Features”. Inside EVs.
    17.   “Model X Owner’s Manual 8.0, page 94” (PDF). Tesla. 2017-08-23. Retrieved 2017-08-23.
    18.   Lambert, Frederic (August 9, 2017). “Tesla has a new Autopilot ‘2.5’ hardware suite with more computing power for autonomous driving”. electrek.co. Retrieved 15 February 2018.
    19.   Hawkins, Andrew. “Tesla has been working on a backup plan in case its self-driving promises fail”. The Verge. Retrieved 15 February 2018.
    20.   Lambert, Fred (2016-02-18). “Tesla pushes a new update with improved ‘Autopark’ and ‘Summon’ feature [v7.1 2.12.22 release notes]”. Electrek. Retrieved 2017-02-23.
    21.   Lambert, Fred (2017-03-29). “Tesla releases 8.1 software update and improves Autopilot 2.0 features: Autosteer 80 mph and Summon”. Electrek. Retrieved 2017-04-01.
    22.   Hall, Gina (2015-12-16). “Tesla to limit self-driving functions”. Silicon Valley Business Journal. Retrieved 2015-12-16.
    23.   “Tesla wants to make self-driving cars a reality by collecting more video data from drivers”. The Verge. 2015-12-16. Retrieved 2017-05-08.



       Model X Owner’s Manual to

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    5.   Musk, Elon (2015-07-31). “The car will learn over time, but there is a min calibre of starting quality”. Twitter. Archived from the original on 2015-11-10. Retrieved 2015-08-06.
    6.   Loveday, Steven (2016-04-29). “Elon Musk “The Probability Of Having An Accident Is 50% Lower If You Have Autopilot On””. Inside EVs. Retrieved 2016-04-29.
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    17.   @elonmusk (2016-07-21). “Autopilot prevents serious injury or death of a pedestrian in NY [sic] (owner anecdote confirmed by vehicle logs)” (Tweet) – via Twitter.
    18.   Etherington, Darrell. “Autopilot in Tesla Model X helps the driver get safely to a hospital”. TechCrunch. Retrieved 2016-08-12.
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    20.   “Lawsuit labels Tesla Autopilot as ‘dangerously defective'”.
    21.   “Dashcam shows fatal Tesla Model S crash in China”. CNet. 15 September 2016. Retrieved 18 October 2016.
    22.    to:a b c Boudette, Neal E. (14 September 2016). “Autopilot Cited in Death of Chinese Tesla Driver”. The New York Times. Retrieved 1 April 2018.
    23.   Lambert, Fred (14 September 2016). “Another fatal Tesla crash reportedly on Autopilot emerges, Model S hits a street sweeper truck – caught on dashcam”. Electrek.
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    33.   Simonite, Tom (2016-07-06). “Tesla’s Dubious Claims About Autopilot’s Safety Record”. MIT Technology Review. US. Retrieved 2016-07-07.
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    Machine Learning and Data Are Fueling a New Kind of Car

    Intel’s proposed $15.3 billion acquisition of Mobileye, an Israeli company that supplies carmakers with a computer-vision technology and advanced driver assistance systems, offers a chance to measure the scale of this rebuild. In particular, it shows how valuable on-the-road data is likely to be in the evolution of automated driving.

    While the price tag might seem steep, especially with so many players in automated driving today, Mobileye has some key technological strengths and strategic advantages. It’s also developing new technologies that could help solidify this position.

    Mobileye uses a single camera, together with a proprietary computer chip and some clever software, to provide various advanced driver assistance features. Its systems can, for example, identify the speed limit from road signs, or identify vehicles and pedestrians for an automatic braking system.

    David Keith, a professor at MIT’s Sloan School of Management who studies technology adoption in the automotive industry, says besides offering a simple, low-cost solution, Mobileye has amassed a huge amount of data—something that is vital to the machine learning that underpins automated driving today. “Their technologies are highly reliable, honed over millions of miles of driving experience, which competitors cannot easily replicate,” he says.

    It isn’t hard to see why Intel should want to enter the auto market. The increasingly capable computers, sensors, and wireless connections now found in vehicles are enabling big changes across that industry. Meanwhile, Intel has seen its position of dominance eroded in recent years as desktop and laptop computers fade in importance, and as different types of computer chips have become more popular. Competitor Nvidia has already captured a sizable share of the growing auto market.

    Mobileye’s technology can automatically identify lane markings and curbs.

    Keith adds that Intel will aim to use its hardware expertise to develop the increasingly sophisticated fusion systems—combining cameras, radar, and possibly laser sensing, or lidar—needed to bring fully automated vehicles to market.

    If your car is capable of identifying a road sign or a pedestrian on the road ahead, there’s a good chance it already uses one of Mobileye’s chips for the task. The company’s vision systems are a simple, low-cost solution that offers surprisingly sophisticated sensing. The company, therefore, offers Intel a good way into the automated driving market, which promises to grow as the technology matures in the coming years.

    For its vision system, Mobileye employs deep learning, a machine-learning technique that has given computers powerful new capabilities in recent years. This involves capturing images as cars drive around and annotating them to identify things like road markings, traffic signs, other vehicles, and pedestrians. The images are fed into a big neural network, which is tweaked until it can reliably recognize the relevant elements of an image. If Mobileye’s system is unable to identify something, it’s usually possible to simply annotate some new images and add them to the learning data set.

    This isn’t to say that it’s perfect, or all that’s needed for automated driving. Tesla had been using Mobileye’s vision technology for its Autopilot semi-automated driving system until last year. The companies ceased working together after a fatal accident involving a car controlled by Autopilot. In the fallout from the crash, the carmaker criticized the vision system provided by Mobileye. Executives from Mobileye countered that its technology was never meant to be used in this way.

    Technology now under development at Mobileye could help automated cars drive more safely in the future. In December, I met with Amnon Shashua, Mobileye’s CTO, and Shai Shalev-Schwartz, VP for technology. They explained how Mobileye is now using reinforcement learning, a technique inspired by the way animals learn through experience, to teach computers how to drive safely in complex and subtle situations (see “10 Breakthrough Technologies 2017: Reinforcement Learning”).

    As part of this effort, Mobileye is developing a simulated driving environment to enable learning. It hopes this will become the standard environment for testing automated driving software. They also explained that Mobileye is working with several carmakers on a way for them to share the data collected with other companies for a price. This could help accelerate (no pun intended) progress toward fully automated driving. The result could be a transformation of transportation as we know it. Indeed, the prospect of profound disruption has caused a stampede for technology and talent among automakers, suppliers, and startups.

    Stephen Zoepf, executive director of the Center for Automotive Research at Stanford, agrees that Intel’s acquisition of Mobileye shows how critical data and machine learning are to the auto industry’s future. But he adds, “It’s also evidence of the degree to which demand for talent is outstripping supply in the autonomous vehicle space.”

    5 ways artificial intelligence is driving the automobile industry

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    Artificial intelligence is taking the automobile industry by storm while all the major automobile players are utilizing their resources and technology to come up with the best. The beauty of devices with artificial intelligence is that it tries to learn from sensory inputs like real sounds and images. In the same way, when intelligence is applied to the technology within an automobile, it would recognize the environment and evaluate the contextual implications when it moves or faces any hurdles. In 2015, the install rate of AI-based systems in new vehicles was just 8%; this number is expected to soar to 109% in 2025. This is because different kinds of AI systems will be installed in vehicles.

    Image result for artificial intelligence in automobile industry

    Artificial Intelligence ‘drives’ driverless cars

    The thought behind driverless cars was around from the 1970s, so it is not entirely new. AI-powered cars, depicted in movies over the years, have always captured our imaginations. But the lack of technical brilliance and resources probably kept it from becoming a reality, until recently. Eventually, all the factors leading to artificial intelligence shaped up and now driverless cars have become a reality. Well, almost, it is just a matter of time before you begin to see real intelligence in them. The idea is to empower the vehicle to act like a human driver and drive through various circumstances. This may sound easy but is in no way a simple task because a lot of careful computing is required.

    Image result for artificial intelligence in automobile industry

    Through techniques like sensor fusion and deep learning, researchers were able to develop a technology that would help build a three-dimensional map of all the activities that happen around the car. Some of the leading tech and automotive giants like Google and Tesla are spending millions of dollars on research to come up with better technology and to make autonomous cars a commercial reality. Recently, Ford Motor Company made an investment of one billion dollars in Argo AI, a new AI company to bring forth a virtual driver system in the future, possibly by 2021.

    Now, let’s look at the different ways in which artificial intelligence will drive the automobile industry in the future:

    1. Machine Learning

    Artificial intelligence is a kind of intelligence developed as a result of excellent scientific experiments. This intelligence, when applied on to devices and machines, will think and act almost like human beings. However, there is a difference between Artificial Intelligence and Machine Learning. While in AI, devices would carry out tasks in a way humans consider smart, ML or Machine Learning is an application of AI where machines are given certain data and they learn for themselves. MI is actually a subset of AI. Toyota has gone a step further and has brought together Big data, Machine Learning and Artificial Intelligence to create highly responsive autonomous systems that aid in mobility for those who are “less able to drive”.

    2. Deep Learning

    Deep Learning is the process by which we implement Machine Learning. It is with the help of Deep Learning that many of the activities in AI happen without setbacks. DL helps break down tasks in manageable chunks. The software in DL learns and then it starts mimicking the activities in the neuron layers of our brain. DL techniques have been very useful in the automobile industry as it aids in advanced driving assistance systems and autonomous driving. This is just within inside the vehicle. DL plays an important outside the vehicle as well – during manufacturing, sales and after service. Even in services where technology was a bit hazy until now, it was DL that brought in a huge improvement.

    Recently, a partnership was formed between Nvidia and Bosch to improve the quality and features of autonomous vehicles. Through this, Bosch will build a supercomputer that will work in such vehicles, and Nvidia will provide the Deep Learning technology to power it. Thanks to both Nvidia and Bosch, in future you can expect a new level of AI touch in cars like Audi and Mercedes Benz.

    3. The Internet of Things

    Internet of Things has made an indelible mark in the automobile industry and is poised to go even stronger in the coming years. Newly manufactured vehicles had a host of new things attached to them – Smart sensors, geo-analytic capabilities with big data, embedded connectivity applications. And with their fancy features, vehicle owners can enjoy a number of features. Here are a few of them:

    • Dealers or manufacturers will make the vehicle’s firmware updates through over-the-air software
    • If a vehicle is scheduled for service or repair on a particular day, performance data of the vehicle would be sent to the manufacturer/dealer/service centre.
    • Through software, the manufacturer can correct certain performance issues in the vehicle; the owner may not have to make a shop list.
    • Companies with fleets of vehicles of their own can manage the same with improved safety parameters.
    • Enhanced manufacturing quality is possible through IoT processes.
    • In case of medical emergencies, the car’s smart sensors would summon the concerned medical personnel

    4. Cognitive Capabilities

    The driverless revolution will continue to cruise along the streets, and you will be witness to not just small cars with AI capabilities, but huge 18-wheel trucks carrying an assortment of goods as well. This is taken a step further with cognitive analysis that kind of imitates the human behaviour by looking at the behaviour patterns and data mining capabilities.

    Cognitive systems are supposed to work just like a human would interpret a real-life situation, and in order to do that, a deeper understanding of unstructured data is essential. Insights would be drawn from plenty of unstructured data to decide on how to respond naturally in real time. Cognitive capabilities would be able to handle dynamic operating conditions as well. Car manufacturers have already started incorporating this into their vehicles. For example, BMW has partnered with IBM to add cognitive capabilities to its cars. Through Big Blue’s Watson AI technology, the idea is to help vehicles communicate with each other.

    5. Infotainment Systems

    Artificial Intelligence changes in-vehicle infotainment systems in a major way. Because of this trend, the demand for high-quality hardware and software solutions has also soared because they all have to be compatible with AI.

    In the infotainment category, you can expect a spike in features like speech recognition, eye tracking, monitoring driving, gesture recognition, and database of natural languages. Eventually, this will also go a step further to consider driver condition evaluation, camera-attached machine vision systems, sensor fusion engine control units and radar-based detection units. There are infotainment human-machine interfaces already attached to vehicles and this can monitor and act according to the algorithms collected from cloud-based neural networks. This would then be used to perform advanced tasks.


    Through Artificial Intelligence capabilities, you can witness a new kind of car driving – the “cloud to car” phenomenon. Thanks to tremendous computational power that’s available at their disposal developers have been able to create apps that have taken artificial Intelligence to a whole new level of excellence. And it is not just about cars that drive on its own, and act as a real driver would in various circumstances, AI would also help in building cheaper cars that can sense the environment and navigate through all the hindrances that may come up during driving.

    Machine learning in the automotive industry

    Most manufacturing operations in automotive industries are still largely dependent on experience-based human decisions. The emergence of Big Data, in conjunction with machine learning in automotive companies, has paved a way that is helping bring operational and business transformations, thereby leading to an increased level of accuracy in decision-making and improved performance.

    The automotive industry continues to face a dynamic set of challenges. Shifting market conditions, increased competition, globalization, cost pressure and volatility are leading to a change in the market landscape. Self-driving cars and changing usage models have heightened customer expectations. It is needless to say that the automotive industry is on the brink of a revolution. One area that has demonstrated an opportunity to deliver significant competitive advantage is analytics. The automobile is getting transformed by technologies. AI and machine learning algorithms have found an increasing level of applicability in this industry. The collaboration of Big Data analytics and machine learning has boosted capacity to process large volumes of data, thereby accelerating the growth of AI systems. Machine learning in the automotive industry has a remarkable ability to bring out hidden relationships among data sets and make predictions.



    Machine learning algorithms can accurately incorporate analysis results of customer feedback in social media, for example, text and tweet analytics. This helps in building vehicle and sub-systems performance for guiding future product design. It also helps in detecting failure patterns for establishing a relationship between the failure and causes of failure. Take an example of an automotive company, that found out that cause of failure in several operations in the car is associated with region-specific issues such as inferior fuel quality, climatic conditions, road infrastructure, and so on. This company can make use of machine learning systems for developing region-specific customizations that can improve product reliability.


    Machine learning algorithms can aid in effective planning and execution of predictive maintenance. Predictive maintenance employs monitoring and prediction modelling for determining the condition of the machine and for predicting what is likely to fail and when it is going to happen. Machine learning systems can help in adjusting maintenance interval, where the same maintenance is conducted but shifted backwards or forward in time or mileage. Thus, machine learning systems can enhance predictive maintenance capabilities and help in the accurate prediction of future failures instead of diagnosing already existing ones.


    Machine learning facilitates personalization and smart personal assistance. It incorporates analysis results and learns traits of user personality, thereby creating user-specific profiles, which can then be leveraged to provide personalization and assistance.

    Machine learning algorithms can be quite useful in solving automotive domain problems, but organizations implementing Big Data analytics and machine learning systems must know how to select the correct algorithm and input/feature vectors for a specific problem domain. Selecting correct feature vectors requires domain experts, and selecting correct algorithms requires experienced data scientists. Once they know how to define the problem domain and business objectives, and validate the selected algorithm in terms of functionality and performance metrics, machine learning systems can accurately demonstrate tangible business benefits.



    5 Ways Big Data Is Changing the Auto Industry

    5 Ways Big Data Is Changing the Auto Industry

    Big data was an interesting concept a decade ago, and now it’s a ubiquitous feature of modern businesses. Data is fundamentally valuable; depending on what you gather and how you use it, data can give you better business insights, help you change direction, and guide you in learning how and why your business works the way it does. When that data is collected on a massive scale, its benefits grow even further.

    Each industry is capitalizing on the spoils of big data a little bit differently, and those new abilities, ideas, and processes are reshaping the industries in new and exciting ways. The automotive industry is a perfect example; from concept to ongoing customer service, big data is fundamentally transforming the auto industry.

    The Auto Industry

    The auto industry is bigger than you might realize. There are big-name auto manufacturers, who design and assemble vehicles for the masses, but you also need to consider the wide network of suppliers they rely on to create and ship the individual parts necessary for those vehicles. There are also distributors responsible for relocating and selling those vehicles and don’t forget departments like safety and customer service. The auto industry is far-reaching, and it uses big data at almost every level.

    Big Changes

    Big data is improving the auto industry in multiple different dimensions:

    1. Value analysis. First, big data is helping companies understand the real values of their cars. This is useful when designing new vehicles, but even more useful when valuing old cars. Valuation services like those provided by Kelley Blue Book are more precise and more efficient than ever before, and vehicle recyclers like the Clunker Junker can offer vehicle owners a more precise sum for their old junkers.
    2. Supply chain management. One of big data’s most important applications is dissecting the value and flow of specific processes across multiple organizations; in the auto industry, this analysis is applied to supply chain management. Companies need to know what parts they’re getting from where, how much they cost, how efficiently they’re being provided, and how those actions affect the profitability of the company overall. Complex data processing allows insight into these dimensions for the first time, and companies are optimizing their strategies accordingly.
    3. Cost reduction. Big data in the auto industry is driving overall costs down. Big data analysis allows companies to understand when one material is substantially beneficial over another and helps them discover new procedural changes that can improve efficiency or maximize productivity. Ultimately, that means companies are capable of putting together vehicles far less expensively, and consumers are seeing the benefits. Consumers end up paying less for vehicles, and vehicle manufacturers still get to maximize their profits.
    4. Safety improvement. Companies are also using big data to delve deeper into analyzing vehicle safety. After collecting millions of data points from both test crashes and simulated scenarios, companies are able to make hundreds of additional improvements to their vehicles to increase their capacity to survive immediate events and long-term wear and tear. This, again, is advantageous to both companies and consumers; consumers get to enjoy a safer vehicle, and companies have happier customers and lower insurance costs. It’s gradually making our roads safer as well.
    5. Consumer understanding. Finally, automakers are using big data to better understand what their customers want and need. This allows them to design more attractive, more practical vehicles for the masses (which gives consumers more of what they’re looking for and increases sales for the manufacturer). It also gives automakers key insights that they can then use to create more specific advertising and marketing campaigns, saving money by increasing efficiency and still maximizing exposure for their most important brands.

    If you own a car or plan on purchasing one in the near future, big data is already benefitting you. Thanks to big data and predictive analytics, our vehicles will grow increasingly inexpensive, safe, and tailored to our individual needs. Complete those customer surveys if you get the chance, and keep contributing to the vast wealth of data that these companies need to keep improving.

    – Larry is an independent business consultant specializing in tech, social media trends, business, and entrepreneurship