Tesla Autonomous Cars

Artificial intelligence (AI) is a subset of scientific and computational technologies that draw inspiration for their unique operations from how individuals learn, think, and act by using their senses and bodies. Tesla self-driving AI vehicles can currently navigate the nation at night while keeping track of their whereabouts. Self-driving cars are expected to hit the market between now and 2021, ushering in a new era, according to a 2016 World Economic Forum report. The digital revolution in the automotive industry will generate $67 billion for that industry and $3.1 trillion for society. That encompasses enhancements from self-directed automobiles, the whole transportation ecosystem not forgetting the connected travelers. Meanwhile, Tesla has initiated Auto steer and Autopilot attributes through a massive fleet of 70,000 cars on roads. Using sophisticated technology, its automated system can maintain the lane, attune its speed with that of traffic, and brake to avert collisions. However, the technology still poses ethical concerns. Notably, the virtue self-driven vehicles is that people can override independent components when they believe there is an impediment or when they identify a new set of situations, which needs human judgment. The following report examines Tesla’s roadmap for self-directed cars, the various innovations the company has deployed to make cars autonomous and benefits and costs of the technology. By examining the ethical concerns, expenses and benefits, this report will recommend what the company can do improve the safety of the cars going into the future.

Artificial Intelligence: Tesla Autonomous Cars

Introduction

The issue

Artificial intelligence (AI) is the process of making machinery intelligent. Intelligence is the quality, which enables an object to function properly and with farsightedness in its setting. Diels and Bos (2016) define an autonomous vehicle as that which can sense its surrounding and drive itself from one area to another without needing human input. Semi-autonomous is considered as a vehicle that has restricted automatic capability including having the capacity to steer, brake, accelerate, stop, switch lanes, and enter parking. A driver is needed to avert accidents and crises. Self-driven automobiles are autonomous cars, which are self-driven and through traffic. The concept appears unbelievable as it may seem but will be the greatest high-tech revolution of the close future. It encompasses using Artificial Intelligence (AI) and Mechatronics to direct the car thus assuming the driver’s responsibilities, giving an additionally controllable management over it. Independent transportation will soon be widespread, as many individual’s first experiences with physically embodied systems of artificial intelligence will significantly affect the perception of the public to the innovations. Tesla’s website provides a claim for “Complete Capability in Self-Driving,” arguing that all one will need to enter inside and instruct your car wherever you want to go (Stewart, 2017). However, the cars do not yet have that ability at least not for now.

The firm’s CEO Elon Musk has asserted that the new cars will have the first-generation Autopilot that will make the cars’ system to maintain its lane and a secure distance from other cars on the road. The illustration of autonomous vehicles on roads in the previous many years mainly Tesla’s Autopilot has fastened motivation in the innovation. The model by Google introduced in 2014 (May), is completely autonomous, and remarkably has no steering wheel, brake, or gas pedals. The Autopilot Tesla introduced in 2015 (October) can work independently on restricted-access roads, but still needs driver input to be ready to assume control, as needed because the car cannot recognize lane marks, cyclists or passersby and cannot switch off automatically.

The importance of the issue to business/society

The issue of AI is important to business and society because people will gain in different ways leading to a unique urban setting. In the typical city of North America in 2030, physically embodied autonomous applications will not be restricted to vehicles but have a likelihood of including personal robots, trucks, and flying vehicles. Reports from the Company covering December 2015 to 2016 November offer interesting insights on the possible economic benefits of producing autonomous vehicles for the mass market. The introduction of the self-driven car models has elicited a spirited discussion about the technological implications of the innovation and the way its development should advance responsibly. Diels and Bos (2016) note that autonomous vehicles would develop novel markets, motivate new efficiencies, and stretch human capacities in extraordinary ways. Moreover, the autonomous vehicles could just address the challenge of traffic, reduce traffic jam, road accidents, and ensure life becomes much easier. Some benefits to the society include reducing accidents as well as economic benefits. The US alone wastes many dollars every year. A paper by Researchers from the Texas University discovered that if 10% of the cars on the driveway were autonomous vehicles, there could be over USD 37 b in savings from fuel and saved lives, decreased travel period, and others. The projected paybacks would surpass USD 447 b if ninety percent of the cars on the ways were autonomous. Autonomous cars could lessen the problem of travelling and energy use. The drop will allow the disabled and the elderly to move with ease. Moreover, it will be significant to examine the ethical, legal, and policy perspectives of the innovation.

Sources used

The primary sources for the paper were derived from Wired.com, which is Tesla automobile’s major website an article from the Forbes Magazine. The assignment has also used scholarly articles from reputable journals such as the Vehicle System Dynamics journals, Applied Ergonomics, the International Journal of Economic Practices, and Theories journal and a dissertation. Information will also be obtained from relevant books, focus on the AI technology of self-driven cars.

Purpose of the paper

The objective of the report is to describe how Tesla’s revolutionary self-driving cars have introduced numerous new uncertainties in daily life in areas of commerce, public policy and how practical solutions and new efficient markets can be generated. The report will also highlight and describe the technology Tesla uses in the self-driven cars thereby increase the awareness of the technology that runs the cars. Such technologies include Tesla’s primary sensors, which are primarily cameras as its primary sensors, as they apply to the autonomously driven automobile industry. Whereas these developments exhilarate many, they may also pose tremendous challenges regarding their legal, social, economic, political, and even ethical perspectives. The report will highlight the costs of AI systems such as the elimination of many jobs and social disturbances. Consequently, the study will recommend essential approaches by Tesla to the issues going into the future.

Discussion of Findings

Road Map for Self-Driven Vehicles

A report prepared by the Market Research Future suggests that the international autonomous car market is anticipated to reach $ 65.3 billion in value by 2027. In January 2016, former President Obama in his State of the Union speech pledged $4 billion in support of autonomous car technology, while Antony Foxx, the Transportation Secretary pledged to expedite legislation to ensure America does not remain behind other states in developing self-driving vehicles. The government of German has also made it a priority to take leadership in autonomous driving given that the auto sector is of critical significance to the image and economy of the state (Newcomb, 2016).

Soon, autonomous vehicles will be the safest cars on the driveway. Presently, driver-aided systems enhance or automate driving operations comprising parking, lane control, navigation, and collision evasion. However, automobile today still need human input in readiness for taking over. Thus, the time is right for autonomous cars as demonstrated by three significant efforts. For example, Google’s self-driven vehicle project has registered over two million miles. Uber is also testing several autonomous Volvos. The company is working on developing neural networks and deep learning technologies, which enable vehicles to process huge capacities of sensor data quicker than done before. Besides, Tesla Autopilot feature, which is a semi-automatic driver assist component, is presently integrated into many Tesla models. In the future, AI and intense learning innovations will optimize advanced sensor technologies and instantaneous route mapping that ensures cars are safer and entirely independent compared to the human-driven cars.

AI will take a critical aspect of self-drive cars. The cars use sensor data to view the scene, road rules, and signs identification, comprehend laws and they continuously enhance functionalities with self-learning ability. In 2014, Newcomb (2016) witnessed firsthand stages the first phases of a Volvo study project dubbed  Drive Me, which will evidently put drivers behind the 100 self-driven XC90 SUVs on public driveways around Gothenburg-Sweden, which is the home of the automaker city to assist studying the innovation. In the same year, the company announced another autonomous driving trial for 100 cars in China. Concerning the Gothenburg project, Volvo stated that local drivers would try independently driven cars on roads in daily driving situations.

Fig. 1: Drive Me Volvo XC90 test vehicle (Newcomb, 2016)

The Tesla Motors CEO has made a projection for the autopilot technology. In the past, the CEO promised electric cars and trips to Mars and established a reputation for attaining these objectives albeit a schedule, which is far behind (Stewart, 2017). Musk is now pledging that come to the 2017 end; he will develop a driverless Tesla that will take itself from Los Angeles to the City of New York. The 2017 timeline puts the company years ahead of other big players working to produce complete autonomous cars. China’s Baidu target is 2019 while Ford’s is 2021. Google and GM have not yet given its schedule, but it is soon. The companies are intending to produce cars, which will remain in restricted areas.

Fig. 2: Tesla’s self-drive Model X SUV

Tesla’s website has already provided a checklist for full self-driving capacity (Stewart, 2017) stating that all one will need is to enter the car and tell the vehicle he or she wishes to go. However, the cars have not yet attained these capabilities. According to Raj Rajkumar, who operates an independent driving research at the University of Carnegie Mellon, an unedited vehicle driving sequence in downtown San Francisco would make more sense to see what the vehicle can do.

Consequently, creating a car, which can address any situation needs intensive teaching. Google leads in this realm. The company’s fleet of sixty self-driving vehicles has covered over two million miles mainly around several cities. Google’s Waymo program continues to outpace the competition (Stewart, 2017).After Tesla has fulfilled its Model 3, it will face another challenge, which will be finding new buyers in a progressively crowded market. The CEO of the company has established a great fan base, but as he endeavors to develop past the enthusiastic early adopters, he will have to deal with new and old competitors. 

The firm’s cars covered 636,000 miles recording just 124 disconnections, a drop in 19 percent from 2015. The company’s fleet logged almost all the recorded miles on the quiet, Mountain View suburban streets and its vicinities, and many of the interventions followed software or hardware incongruities, when, maybe, the vehicle’s camera and lidar documented slightly different data.

However, Tesla has an advantage; its automobiles have covered more than 222 million miles under the Autopilot mode comprehensively gathering data the entire time (Stewart, 2017). According to Jeffrey Miller a member of IEEE who studies self-driving at the Southern California University, the company is getting more owing to the number of cars they have sold. Google’s vehicles capture the world using laser sensors, camera, and radar alongside a human engineer to flag the critical stuff (Altun, 2015). 

The Role of Geomatics in Tesla’s Autonomous Driving

According to Dimitrakopoulos (2017), self-driving would not be possible without geomatics innovations. Driving without the driver requires a complex set of innovative functionalities that sense the happenings, map the route, and establish driving policies that address predictable and unpredictable happenings.

Global Navigation Satellite System (GNSS). Autonomous vehicles can navigate on their own, which already suggests the involvement of geomatics. GNSS innovation provides the accuracy, which a car needs to be autonomous. A reliable and high-precision localization solution is critical. One can imagine, for instance, what could occur if weak localization places the car on the wrong pavement of the road. Thus, the availability of reliable and accurate GNSS innovation poses an enormous challenge to developing advancement of autonomous driving. Tesla uses the well-developed GNSS systems in its autonomous cars. The receivers depend on different frequencies and use compound constellations. Its positioning is integrated with an inertial navigation model (INS), creating a robust system that compensates for the inherent weaknesses that arise when one system is in use (Dimitrakopoulos, 2017). In addition, anti-jam innovation is used to provide the required sensors positioning and integration. The company’s GNSS specialist argues that the technology can provide high-level accuracy.

Light detection and ranging technology (LIDAR Technology). The main advantage of autonomous cars is the merit they have over humans in tuning out distractions. Yelling kids, vibrating phone or daydreaming will distract focus from their core task. That does not necessarily suggest that they cannot be overwhelmed with issues in the same way people are. The completely autonomous cars, which businesses like Ford, Baidu and Google are aggressively coming up with technologies that depend on LIDAR technology to observe and map the environment. The maps are important because they provide critical context for the cars and allow them to focus their sensors and computation power on provisional obstacles such as cyclists, cars, and pedestrians. Tesla depends on radar and Lidar in its autonomous vehicles to cross-validate whatever is within their view and project motion (Gordon & Lidberg, 2015). Elon Musk, the CEO of the company is sticking to modern radar integrated with ultrasonic sensors. AI Ford is not only laying focus on Lidar technology. The firm declared in February 2015 that it would invest USD1 billion in the coming years in Argo Artificial Intelligence firm fascinatingly developed by Uber leaders and Google. The concept underlying the massive investment is that Argo AI’s computing experience and AI software is needed to advance driverless cars further (Gordon & Lidberg, 2015). The primary objective of this collaboration is the operation is a novel software foundation for Ford’s completely autonomous car to be introduced in 2021.

Lidar has a challenge that similar to human eyeballs; it does not just recognize the related stuff. It sees stop signs and lane lines and also records leaves on trees, garbage bins in driveways, and windows on constructions. These observations establish a cluttered map thus according to Sravan Puttagunta, the CEO of Civil Maps, it is not very operational (Stewart, 2017). The chief executive officer of Tesla is avoiding widely used LIDAR sensor innovation that fires out laser pulses to generate a super accurate graphic, environmental representation. Musk has often disliked it and its cost. Google engineers say that the cost of the technology is randomly $80,000. Nonetheless, Tesla is furnishing its vehicles with radar that faces forward and eight cameras, which Musk state will deliver a great view of the universe (Stewart, 2017).

According to Miller, LIDAR is a good innovation but it has moving parts that make it susceptible to fail, and besides, it is a costly innovation. Unlike radar, it experiences challenges in fog, rain, and snow (Stewart, 2017). No one has a perfectly safe solution without LIDAR. Tesla might create a car, which is twice as much, safer than a human driver; thus, LIDAR is the lone solution that guarantees 100 % safety. Tesla is equipping its vehicles with a super computer that optimizes deep learning to translate data from the sensor into driving instruction. The name of the sensor is Nvidia’s Drive PX 2, and it teaches the vehicle to handle itself.

3D Maps. The development of self-driven cars has driven the assortment of point clouds around the globe. Maps for self-driven cars ought to provide more data with a greater accuracy and fidelity comprising elements like roadside obstacles and lane markings. Map datasets of high-definition deliver high-precision road features of intelligence such as 3D construction models, painted lines, signals, signs, parking slots, and stop signs. Self-healing map systems provide a sophisticated solution for Tesla’s self-driven cars. They address the challenge of old navigation data because they give vehicles the intelligence to appraise their maps. Self-driven vehicles can acquire and process data and turn it into valuable information while on the driveway. Additionally, the cars will be linked with the cloud to make the right decisions regarding wherever they are going comprising selecting the optimal route. Tesla has used a mapping system for its self-driven cars, which enables it to develop HD maps and to update them.

Optical Cameras. Different camera technologies are being integrated into the autonomous vehicle industry, each having its advocates. Google’s vehicles capture the universe with laser sensors, radar, and camera alongside a human engineer to flag the critical issues. On the other hand, Tesla optimizes cameras as a basic sensor and has eight monocular cameras to its cars. Apart from the eight cameras, it has equipped the cars with forward-facing radar that provide a good a view of the universe (Stewart, 2017). On the other hand, stereo cameras, give the vehicle depth of field, which can be likened to human vision (Bunghez, 2015). Additionally, the stereo cameras offer the merit of being less costly to generate, and they provide high-value measurements instantly. Nonetheless, some argue that fisheye cameras provide an even better option as these can cover a wider angle of view including detecting hindrances in the car’s close vicinity that are not often viewed by a binocular stereo camera system.

Radar. Radar technology, a fundamental element in Tesla’s self-driven cars augments the passenger’s safety. The radar sensors are attached to the vehicle’s rear and front a bumper, giving the vehicle intelligence of what lays behind and in front it. The car will remain at a safe distance (by two seconds) from the one ahead. The integration of radar technology makes the car to speed up or slow down automatically in line with other vehicle’s behavior. Radar, in fact, observes the altering distances between the car and other automobiles. Tesla has integrated the software, which interprets the data sending a signal, which the vehicle requires to go faster or slow down. A current development in the radar technology field is V2X (Vehicle to everything) radar, which combines car-to-car communication and car-to-infrastructure innovation while running on one antenna. Radar’s is advantageous in comparison to other technological inventions. It can handle cope with weather conditions such as fog, snow and heavy rain (Stewart, 2017). V2X can detect vehicle speeds instantly because of Doppler measurements, and360-degree detecting from one antenna.

Potential Benefits and Costs of Autonomous Cars

Benefits. The use of autonomous vehicles generates various benefits and costs. Developments in predictive analytics and AI will expedite new service configurations that will allow customers to be taken from one door to another with almost no breaks in a shared and comfortable car. A Smartphone application will hail the services. The service could even be linked directly to the users’ digital calendars. Experts in developed economies have projected an imminent change to commercial-based transport services on a mobility service model that will precipitate a decline in individual ownership of cars and the upsurge of a service that uses Uber-like features. Service providers will rise to clean, service, and sustain the autonomous cars. Service differentiators will be vehicle comfort, safety records, ownership cost, and connectivity.

Proponents argue that autonomous cars will provide critical user safety, convenience, reduced congestion, energy savings, and decreased of pollution. Many of these claims seem overstated. For instance, advocates claim that because driver error makes up for over 90 percent of traffic accidents, autonomous cars will minimize crashes comprising system failures, counteracting behavior such as the inclination of road users to assume extra risks whenever they feel secure, cyberterrorism, and rebound effects (Koehler, Appel, & Beck, 2017).

The major merit stemming from the use of artificially intelligent robotics to drive will be a reduction in the driver’s errors thus resulting in various socio-economic gains. These include cost savings, as the lack of drivers would make extra journey cost effective per kilometer. The Tesla cars will also save lives. Every year, about 30,000 to 40,000 individuals are killed on the U.S. roads alone. Many of the accidents arise out of mechanical failure or driving error. In turn, driving errors are caused by lack of awareness, failure to adhere to traffic rules, the distraction of drivers or fatigue. Drunk driving could end. Autonomous cars could also fasten the change to electric cars. Vehicle occupants may also minimize using seatbelts if they feel safe. Other pedestrians using roads could become less wary; cars may function faster and in unity, and human drivers might be interested in joining self-directed car platoons that will initiate new enforcement and risk requirements(Koehler, Appel, & Beck, 2017).

California’s Silicon Valley is prepared for the benefits of Autonomous driving. The Motor Vehicles Department of California proposed new guidelines, which will evidently prepare Tesla and other autonomous cars to move from trials to commercialization. The state of California has good reasons to welcome the innovation. About 3,000 individuals die on its driveways each year. Self-driven vehicles could eradicate the human error, which results in 90 % of crashes. The vehicles could transport more people, minimize emissions, and improve the economy.

The major players in the market are GM, Google, BMW, Ford Motor firm, Baidu (China), Tesla, Toyota, Volkswagen (Germany), Jaguar, Audi among others (The World Economic Forum, 2016).The autonomous car industry is anticipated to USD 65.3 billion when the 2016-2027 forecast period ends (Market Research Future, 2016). It is expected that Radar Sensors will be the fastest developing sector with high CAGR of 29 % between 2016 and 2027. North America has the biggest market share of international Autonomous Cars market at 39.08% and is anticipated to be about USD24 billion by 2027 from (2015)USD 1.4 billion. The Asia-Pacific industry is projected to be the fastest developing market. It is anticipated to increase at a rate of 29% from 2016 to 2026. Presently, all the models are being tried in the R&D centers of different universities and automobile firms. It is expected that the self-driven vehicles will be launched by 2020. 

Fig3: Market Share of Autonomous Vehicle

 Tesla’s cars will lead to savings in gasoline. In America alone, cars consume 143 billion gallons of oil every year at a cost that surpasses USD 500 billion. Self-directed vehicles grounded on predictive capacity and the capability of altering the condition of the car in line with expected load conditions could be additionally efficient compared to run cars manually. The mere use of a cruise control in a vehicle of today can culminate in a 15-30 per cent improvement in the economy against the manually functioning the throttle. The reason is that the car is aware of the type of load that will be placed on the engine and adjusts accordingly. In the future, autonomous vehicles with (V2V) vehicle to vehicle and vehicle to infrastructure communication capability will have a far greater comprehension of traffic and road conditions and should predict even expected loads on the engine enabling them to function in ”cruise” mode most of the time. Self-driven taxis and self-parking vehicles may augment empty vehicle travel. Even though the additional car travel provides benefits to the users, it can augment external expenses comprising parking and roadway facility costs, congestion, a risk of accident imposed on pedestrians and pollution discharges. Strategies including platooning may be restricted to grade-separated roads thus human-driven cars may augment congestion on streets. Autonomous cars may decrease the demand for public transit travel culminating in reduced service, as well as the stimulation of more development patterns that decrease transport alternatives and augment total car travel.

Ethical Concerns, Potential Costs, and Obstacles to Adoption

The autonomous cars pose an ethical issue. The vehicles raise two types of ethical issues. One issue is the challenge of programming an autonomous vehicle to react to each conceivable situation on the road, comprising situations when it may be essential to circumvent or break existing rules to attain a favorable outcome. Another issue is that whereas autonomous cars will likely deliver socio-economic gains, there is also a negative side about rendering some jobs obsolete. The incremental expenses of producing self-driven cars are unclear. They need different controls, special sensors, and computers that presently cost thousands of dollar but have the potential to become cheaper once produced in mass. For instance, Tesla’s Lidar technology is exorbitantly high (Koehler, Appel, & Beck, 2017). Given that failures of systems could be fatal to both the occupant of the vehicle and other users of the roads, all essential constituents will require attaining high standards in manufacturing, repairing, installing, testing, and maintenance and will probably be comparatively expensive. Autonomous car operation may need special mapping and navigation service subscriptions. The issue explains the interest of Google Corporation in the technology. Other, modest technologies integrate hundred thousands of dollars to car retail costs. For instance, review cameras, automatic transmissions, GPS, and telecommunications system, which often cost between $500 and $2,000.

Autonomous car manufacturers will necessitate recovering development costs, progressive service (software upgrades and unique mapping) as well as liability and earning profits. The issue means that when the technology matures, the capability of autonomous-driving will likely incorporate thousands of dollars to car buying prices including some dollars in yearly costs of service increasing between $1,000 and USD 3,000 to annual car costs. Insurance and fuel savings may slightly offset the incremental expenses. The costs average about USD 2,000 for fuel and USD 1,000 for insurance for each car per year (Dimitrakopoulos, 2017).If self-driven cars decrease fuel consumption by ten percent and insurance expenses by 30 percent, the yearly savings will be approximately USD 500 an amount that will not completely offset projected incremental annual costs. Autonomous cars can be programmed to use occupant comfort. Koehler and Beck (2017) argue that because car passengers incline to be additionally sensitive to acceleration compared to drivers, and occupiers optimize travel time to rest or work. Self-driven car illustrations often show occupants sleeping or playing cards. Thus, it is reasonable that for the sake of comfort, users will program their car for deceleration or lower acceleration attributes than human-operated cars culminating in reduced total capacity of the urban roadway.

Tesla has met criticism regarding its Autopilot system, which is considered as not capable as many would believe. Europe has faced challenges in improving the capacity of AI to self-driven cars. In May 2016, a model S of Tesla experienced the first self-driven car fatality in Florida (Stewart, 2017). The driver failed to override the semi-independent component and was killed when his car caused an accident thus subjecting the firm to government evaluation for highway security. Uber grounded its first rejection to apply for an autonomous permit on the premise that its cars necessitate human backups and as such, they do not meet the definition of the state of ‘autonomous.’ The issue presents a challenge because passengers and as car buyers ponder whether to trust the novel technology.

Many clients may at first show reluctance to putting their lives in the robot’s hands. Latest surveys and studies have demonstrated a split in perspective on whether individuals would like the independent capability to be present in their cars or not. Thus, mass acceptance of the technology could take longer, and this could be the situation especially if there are accidents that involve even semiautonomous cars early in the phase of adoption phase, whether it was the error of the independent structure or not.

The practical challenges to wide autonomous vehicles adoption may be great. Nevertheless, to get to this point, the various arising technological challenges need to be solved first. Numerous technological challenges may hinder the path to completely autonomous cars. Worries have been expressed that the eagerness in implementing and improving AI systems by Tesla could push the innovation aback. A serious concern is that Tesla prematurely and brashly introduces innovations, which are not yet set for deployment, encounter fatalities and crashes and triggers substantial pushback from the larger society and perhaps much stringent regulation. Consequently, the U.S. state and Federal regulators are gradually developing legislations to control autonomous innovations. Tesla anticipates upgrading the autonomous capabilities of its cars slowly. One update could incorporate the capacity to observe traffic signals. Another could permit an empty car to look for a parking slot in a private park at low speed. The question asked is, when does the vehicle car become ”autonomous,” and how do the present and future legislations govern its conduct?

Conclusions

Artificial Intelligence (AI) concerns the scientific, computational technologies, which are characterized by their different operations from the way individuals use their senses and bodies to learn, think, and take action. Whereas the rate of uptake of AI has been sporadic and random, several developments have been made in the field in the past sixty years. The AI in the modern world allows a constellation of mainstream innovations, which have a substantive effect on daily lives. Intelligent machines run by AI computers that can learn reason and connect with individuals is no longer scientific fiction. The main players in the market are GM, Google, BMW Ford Motor Company, Baidu, Tesla, Toyota, Volkswagen, Audi, Jaguar among others. Tesla has initiated Auto steer and Autopilot attributes in its AI self-driven huge fleet of 70,000 cars on roads. The CEO of Tesla Elon Musk has said that the new cars’first-generation Autopilot will ensure the cars’ system maintain its lane and takes a secure distance from other cars on the road.

Self-driving would not be possible without geomatics innovations. Tesla uses innovations such as GNSS, Lidar, cameras, 3D maps and among others. Tesla depends on radar and Lidar in its autonomous vehicles to cross-validate whatever is within their view and project motion. However, Lidar poses a challenge in that similar to human eyeballs; it does not just recognize the pertinent stuff. The autonomous car sector will generate critical economic benefits. For instance, the Market Research Future predicts that the inte...