As the world’s population is growing and time lost to traffic snarls is increasing, the importance of autonomous vehicle technology is being realized. Further experts predict another billion people in society in 10 years which will further lead to traffic problems and parking requirements.
Autonomous vehicles will ease congestion, shorten commutes, reduce fuel consumption, slow global warming, enhance accessibility, liberate parking spaces for better uses, and improve public health and social equity. Analysts predict that by 2050 self-driving cars will save 59,000 lives and 250 million commuting hours annually and support a new “passenger economy” worth $7 trillion USD.
Most global auto manufacturers are actively developing autonomous-vehicle technology, including Google, General Motors, Ford, Volkswagen, Toyota, Honda, Tesla, Volvo, and BMW. They are trying to make drivers obsolete, handing control of the wheel to a computer that can make intelligent decisions about when to turn and how to brake.
It’s taking much longer than initially promised, but analysts expect autonomous vehicles to mature quickly over the next decade. Meanwhile, the coronavirus has both strengthened the use case for robot drivers while the demand has grown for delivery of groceries or medicines, the economic fallout from the pandemic will undoubtedly delay the plans of some companies.
As the major manufacturers continue their development efforts, several significant potential benefits are guiding autonomous driving. An Infiniti Research report details the trends it sees steering the industry:
- Real-time route optimization: Autonomous vehicles connect with other vehicles and the traffic management infrastructure to incorporate real-time information on road conditions and traffic levels into route selection.
- Increased lane capacity: Autonomous vehicles can operate at higher speeds and closer vehicle proximity, leading to greater lane capacity.
- Reduced energy consumption: Autonomous vehicles are lighter than conventional vehicles, so they consume less fuel.
- Increased safety for human passengers will be another major benefit.
The rise of driverless vehicles is going to have a major impact on businesses and professionals. Automated vehicles could replace corporate fleets for deliveries or transporting employees, for example. And workers could gain productive hours in the day by working instead of driving during daily commutes. Innovations in this field are also poised to completely change the car insurance industry by reducing accidents—a new report predicts that accidents will drop by 80% by 2040.
“Anyone who focuses solely on the technology has not yet grasped how autonomous driving will change our society,” emphasizes Dr Dieter Zetsche, Chairman of the Board of Management of Daimler AG and Head of Mercedes-Benz Cars. “The car is growing beyond its role as a mere means of transport and will ultimately become a mobile living space.”
Autonomous driving levels 0 to 5
The National Highway Traffic Safety Administration adopted the Society of Automotive Engineers’ levels for automated driving systems, ranging from complete driver control to full autonomy.
Level 0: Level 0 autonomy means everything from steering, brakes, throttle, power is controlled by driver (human) .
Level 1: This driver-assistance level means that most functions are still controlled by the driver, but a specific function (like steering or accelerating) can be done automatically by the car. Level 1 automation is a common feature in most of the current car models of major automakers, like Audi, BMW, and Mercedes-Benz.
Level 2: In level 2, at least one driver assistance system of “both steering and acceleration/ deceleration using information about the driving environment” is automated, like cruise control and lane-centering. It means that the “driver is disengaged from physically operating the vehicle by having his or her hands off the steering wheel AND foot off pedal at the same time,” according to the SAE. The driver must still always be ready to take control of the vehicle, however. Level 2 include Models, like Volvo Pilot Assist, Mercedes-Benz Drive Pilot, Tesla Autopilot, and Cadillac Super Cruise, have been supplied with level 2 automation features.
Level 3: Level 3 automation is referred to as conditional automation. Drivers are still necessary in level 3 cars, but are able to completely shift “safety-critical functions” to the vehicle, under certain traffic or environmental conditions. It means that the driver is still present and will intervene if necessary, but is not required to monitor the situation in the same way it does for the previous levels.
In level 3 automation, the autonomous cars driving system performs all the dynamic driving tasks with the expectation that the human driver will respond appropriately to a request to intervene. The dynamic driving task includes steering, breaking, accelerating, changing lanes, and monitoring the vehicle, along with responding to events happening on the road.
Following the SAE (Society of Automotive Engineers) International automated driving standards, cars with level 1-3 automation features have been considered under the market segment of semi-autonomous vehicles.
Level 4: This is what is meant by “fully autonomous.” Level 4 vehicles are “designed to perform all safety-critical driving functions and monitor roadway conditions for an entire trip.” However, it’s important to note that this is limited to the “operational design domain (ODD)” of the vehicle—meaning it does not cover every driving scenario.
Level 5: This refers to a fully-autonomous system that expects the vehicle’s performance to equal that of a human driver, in every driving scenario—including extreme environments like dirt roads that are unlikely to be navigated by driverless vehicles in the near future.
The autonomous (driverless) car market was valued at USD 20. 97 billion in 2020, expected to reach USD 61. 93 billion in 2026 and projected to reach US$ 206.94 Bn by 2031.
The recent technological advancements in the fields of artificial intelligence, machine learning, and other sensors like RADAR, LIDAR, GPS, and computer vision, have enabled manufacturers to increase self-driving capabilities in cars. Though there are varying levels of autonomy, major players are working towards more advanced control systems integrated into the car that can interpret the sensory inputs to detect signboards or avoid collisions.
Currently, most of the autonomous cars in the market belong to Level 2 and Level 3 which have advanced driver assistance systems, like collision detection, lane departure warning, and adaptive cruise control. Although Level 4 and Level 5 (as scaled by SAE) autonomous cars are unlikely to reach wide acceptance, by 2030, it is expected that there would be rapid growth for Level 2 and Level 3 autonomous cars until then. Fully autonomous cars will not reach a wide customer base unless they are entirely safe from cyber-attacks. If such concerns are addressed, the autonomous car market is estimated to have positive growth in the coming years.
The principal components of autonomous vehicles are sensors and cameras, Lidar acts as an eye for self-driving vehicles as it provides a 360-degree view of the surrounding, which helps vehicles drive on their own, safely. It is used by many autonomous vehicles to navigate environments in real-time. Its advantages include accurate depth perception, which allows LiDAR to know the distance to an object to within a few centimeters, up to 60 meters away. It’s also highly suitable for 3D mapping, which means returning vehicles can then navigate the environment predictably a significant benefit for most self-driving technologies.
Race among Auto manufacturers to deliver increasingly autonomous vehicles.
Major automaker companies, technology giants, and specialist start-ups have invested more than USD 50 billion over the past five years to develop autonomous vehicle (AV) technology, with 70% of the money coming from outside the automotive industry.
Some of the key players in the autonomous car market are Visteon Corporation, Mobileye, Texas Instruments Inc., AB Volvo, Robert Bosch GmbH, Autoliv Inc., Waymo LLC, Toyota Motors, Volkswagen AG, Daimler AG, Ford Motor Company, General Motors, and General Motors
North America is expected to dominate the market in the forecast period. Owing to factors like strong and established automotive company clusters and also being the home for the world’s biggest technology companies like Google, Microsoft, Apple, etc the region has been a pioneer regard to autonomous vehicles. Particularly in the United States, self-driving cars have already been tested and used in California, Texas, Arizona, Washington, Michigan, and other states of the United States. However, their mobility is currently restricted to specific test areas and driving conditions.
Following North America, Asia-Pacific is also expected to have a positive growth in the forecast period due to the factors like China being the biggest automotive market in the world, Manufacturing capabilities of countries like Japan, South Korea, and also emerging economies like India.
Many in Silicon Valley promised that self-driving cars would be a common sight by 2021. Now the industry is resetting expectations and settling in for years of more work. To date, the only platform taking passengers in fully driverless vehicles is Waymo, a Google spinoff. It is the sector’s presumed front-runner. Waymo, the self-driving unit of Google parent Alphabet, was granted permission to operate fully driverless cars without human drivers behind the steering wheel on public roads in California. The company is the first to receive a driverless permit in the state. Google has updated its prototype self-driving vehicle to make it road worthy, adding headlights and manual steering and braking to comply with road rules. “In the six years of our project, we’ve been involved in 16 minor accidents during more than 2 million miles of autonomous and manual driving combined. Not once was the self-driving car the cause of the accident.”
Many automakers are teaming up with the local ride hailing companies to deploying their vehicle in the autonomous taxi fleet. For instance, in April 2021, Volvo Cars and DiDi Autonomous Driving, signed a strategic collaboration agreement on autonomous vehicles for DiDi’s self-driving test fleet. Volvo Cars will be providing DiDi with XC90 cars equipped with necessary backup systems for functions such as steering and braking; and along with that automaker will collaborate with DiDi Autonomous Driving to integrate the additional software and hardware required for autonomous drive.
Toyota and two partners will invest $2.8 billion in the coming years in the Toyota Research Institute-Advanced Development, which will be charged with developing automated-driving technology. In 2018, BMW opened a 248,000 square-foot facility outside Munich, Germany, to develop and test autonomous vehicles.
The industry saw a period of unprecedented acceleration in 2021, with over $8.5 billion invested in robotaxi startups, self-driving truck developers, lidar makers, smart electric car manufacturers, and chipmakers focused on vehicle automation, according to Crunchbase.
Driverless vehicle development is a key part of Beijing’s “Made in China 2025” plan. The space is extremely competitive in China with technology giants form Baidu and DiDi Chuxing, to start-ups like Pony.ai and WeRide.ai developing the technology. Many experts say China could be first to deploy autonomous vehicles at scale — and one indicator is how they’ve already taken the global lead in electric vehicles thanks to government policies and consumer attitudes.
In a bid to lead the race to fully-autonomous vehicles, China is building highways with dedicated lanes for self-driving cars. A new 62-mile stretch of freeway will have two lanes dedicated to autonomous vehicles (AVs), according to FutureCar. The idea is that the infrastructure investment will give AVs access to real-world traffic conditions — but also that the separate lanes will ensure that the still-limited AV tech is tested in a way that minimizes risks for human drivers.
Pony.ai, one of China’s most valuable driverless car start-ups, has launched an app that allows users to hail an autonomous taxi, making it one of the first companies to do so.
Chinese scientists working on game-changing smart driving system for military vehicles
A Beijing lab is developing a smart system for military vehicles that researchers believe could revolutionize driving in all kinds of terrain and conditions, reports said Tuesday.A microchip a few centimeters in size empowers the system to realize accurate real-time sensing of the driving environment, researchers at the Second Academy of the China Aerospace Science and Industry Corporation told Science and Technology Daily on Tuesday.
Its deep neural network can help the driver to detect blurry objects and obstacles ahead by filtering out distractions such as backlighting and shadows, the Beijing-based newspaper reported.The team is working on an intelligent sensor to enable military vehicles to move at night without lighting or in changeable lighting, the paper said.The sensor will also perform in all weathers by combining and computing algorithmic data about visible light, infrared and millimeter wave radar, the report said.
“The sensor can help reduce driver misjudgment in extreme conditions such as a dark environment and complex terrain, which can greatly improve the movement speed,” Song Zhongping, a military expert and TV commentator, told the Global Times.Drivers should still attain a high capability to judge a complicated situation, Song noted, and should not rely on the assistance even though the smart system should boost safety.The system will enter small-scale production by the end of 2018, project team leader Guo Rui was quoted as saying.
The accuracy of the intelligent driving system was 90.05 percent and it took 0.03 seconds to process an image whereas the record is 90.55 percent and 4 seconds, the report said.The technology could improve safety in civilian vehicles, Jia Xinguang, executive director at the China Automobile Dealers Association, told the Global Times on Tuesday.”For example, the car can automatically stop when it encounters a pedestrian or barriers even in bad conditions such as dark night where a human possibly might not notice.” Jia suggested research begin on cutting costs for civilian use.
Apple showed off a self-driving tech breakthrough in obstacle detection
Apple researchers Yin Zhou and Oncel Tuzel, who are AI and machine learning in the company,have devised what they’re calling VoxelNet, an architecture for detecting small obstacles using the Light Detection and Ranging (LiDAR) sensing method. The researchers note that VoxelNet is better than state-of-the-art LiDAR-based systems at spotting not just cars, but also pedestrians and cyclists. They explained:
VoxelNet divides a point cloud into equally spaced 3D voxels and transforms a group of points within each voxel into a unified feature representation through the newly introduced voxel feature encoding (VFE) layer. In this way, the point cloud is encoded as a descriptive volumetric representation, which is then connected to a RPN to generate detections.
Experiments on the KITTI car detection benchmark show that VoxelNet outperforms the state-of-the-art LiDAR based 3D detection methods by a large margin. Furthermore, our network learns an effective discriminative representation of objects with various geometries, leading to encouraging results in 3D detection of pedestrians and cyclists, based on only LiDAR.
Magna Reveals Autonomy-Enabling Radar
Magna has introduced a military-derived high-resolution radar system, the Icon Radar that enables precise image detection at more than 1,000 feet (about 300 m), continuously scanning to determine distance, height, depth and speed, says Swamy Kotagiri, chief technology officer-Magna.
Kotagiri says Icon Radar tracks nearly 100 times more objects than current radar systems, can distinguish between static and moving objects and can differentiate objects such as vehicles, bicycles, pedestrians and animals. The next-generation radar isn’t hampered by weather conditions or other interference.
Magna says the system scans 50 times faster than a human eye can blink, giving the vehicle constant information on complex surroundings, enabling instant decision making. It also can differentiate smaller, closer objects even when larger, more distant objects might reflect a stronger signal. Magna and technology startup Uhnder are engineering and validating the system with a plan to have it market-ready by 2019 and installed in production vehicles in 2020.
Mobileye has developed the sensors and software that allow a car to know where it is in relation to its surroundings. Mobileye-powered autonomous Ford Fusion car is equipped with 12 cameras, radar and scanners that give it a 180-degree view from a distance of up to 300 meters. The technology – dubbed REM (for “road experience management”) – uses Mobileye sensors to draw high-definition maps of road conditions in near real time, crucial for both fully autonomous driving and the advanced safety systems of today’s cars.
SABIC introduces new radar-absorbing compounds for radar sensors
Radar sensors are widely used in advanced driver assistance systems (ADAS), where they provide capabilities such as blind spot detection, collision avoidance, automatic braking and traffic alerts. Radar-absorbing materials (RAM) are used to shield the field of radar wave transmission and attenuate side waves that can cause ghost images or trigger false actions or alarms. A broader choice of radar absorbing LNP STAT-KON compounds can help manufacturers to increase flexibility in sensor positioning and function and to help design sensors that can be optimized for vehicle size and other variables.
The new grades, based on polybutylene terephthalate (PBT) resin, may be used for integration with radomes manufactured using PBT material, which can provide superior resistance to automotive chemicals. They complement and extend SABIC’s existing radar absorbing LNP STAT-KON compounds, which are based on polyetherimide (PEI) resin for withstanding higher processing temperatures or on polycarbonate (PC) resin for general applications that require high durability and a balance of physical properties. The high radio frequency (RF) absorption of these compounds can help increase detection range and improve signal resolution.
“Radar sensors are a critical component of the ADAS suite, in part because they can operate in conditions such as poor visibility that impair LiDAR and camera functioning,” said Jeff Xu, LNP Product Manager, SABIC. “To support technology advancement in the radar sensor market, which is rapidly growing, SABIC continues to develop specialty materials that can enhance sensor accuracy and reliability. Our growing portfolio of LNP compounds offers high absorption of radar waves as well as potential cost benefits compared to radar absorbing materials designed for military applications.”
Race to fully autonomous vehicles
Auto manufacturers are racing to deliver increasingly autonomous vehicles. Former Ford CEO Mark Fields has stated that Ford plans to have a level 4 vehicle by the year 2021 with “no gas pedal, no steering wheel, and the passenger will never need to take control of the vehicle in a predefined area.” Volvo plans to roll out a fully autonomous car the same year.
Now the pursuit of autonomous cars is undergoing a reset. Companies like Uber and Lyft, worried about blowing through their cash in pursuit of autonomous technology, have tapped out. Only the deepest-pocketed outfits like Waymo, which is a subsidiary of Google’s parent company, Alphabet; auto giants; and a handful of start-ups are managing to stay in the game.
In October 2021, Waymo reached a notable milestone: It started the world’s first “fully autonomous” taxi service. In the suburbs of Phoenix, anyone can now ride in a minivan with no driver behind the wheel. But that does not mean the company will immediately deploy its technology in other parts of the country.
The tech and auto giants could still toil for years on their driverless car projects. Each will spend an additional $6 billion to $10 billion before the technology becomes commonplace — sometime around the end of the decade, according to estimates from Pitchbook, a research firm that tracks financial activity. But even that prediction might be overly optimistic.
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