Vehicle Autonomy through Machine Learning
Authors : Dr. Rashmi Ashtagi, Aditya Thombre, Yash Gaikwad, Dakshata wasnik, Vineeth Naitam
Introduction
Although autonomous driving is still in its infancy, technology is developing quickly. The objective is to develop cars that can operate effectively and safely without a human driver.
Autonomous driving has a number of potential advantages, including increased convenience, effectiveness, and safety. For instance, self-driving automobiles may contribute to a decrease in traffic jams, congestion, and pollutants.
Although the technology is still being researched and improved, a number of businesses are now testing autonomous vehicles on public roads. As technology advances, we’ll probably start to see an increase in the number of these vehicles on the road.
Companies working on Autonomous Driving
There are many companies doing great work in the autonomous vehicles space, here are just ten of them that I would like to highlight.
- Tesla
When it comes to autonomous vehicles, there is one business that probably doesn’t require an introduction because it has been in the press a lot for both the good and the bad of its autonomous systems.
Tesla is developing The Autopilot, an automated driving system that will enable its vehicles to drive themselves. In terms of what is already on the road, Tesla’s autopilot technology is still far from fully self-driving because the drivers must maintain their grip on the steering wheel and pay attention to the road.
Perhaps the most cutting-edge AV systems now used on public roadways belong to Tesla.
2. Rivian
The American automotive and energy company Rivian was founded in 2009. The company wants to produce products and services that inspire customers to live sustainably. Rivian is well known for its work on electric vehicles and automated driving. Rivian has been working on autonomous vehicle technologies and has partnered with Amazon to develop a fleet of self-driving cars.
3. Uber
Companies like Uber are prepared for a world where self-driving cars are the standard as the technology becomes more common. Uber has been testing autonomous vehicles in Phoenix and Pittsburgh, and the company intends to introduce a fleet of these vehicles in San Francisco.
Uber is banking that autonomous vehicles will someday change the ridesharing industry, despite the fact that the technology is still in its early stages.
4. Ford
There is no need to introduce Ford as an automaker; nevertheless, the business has been engaged in autonomous car development.
Over the next ten years, Ford will invest about $7 billion in self-driving technologies. In collaboration with Argo AI, the business has been testing driverless vehicles in a number of American locations. Ford intends to dedicate $5 billion of its $7 billion budget to driverless vehicles starting in 2021.
The levels of autonomous vehicles
There are six levels of automated driving, with level 0 being no automation and level 5 meaning complete automation.
Level 0: No automation. The driver is in full control of the vehicle.
Example: Traditional cars.
Level 1: Function-specific automation. The vehicle has some automation features, but the driver is still in control.
Example: Adaptive cruise control.
Level 2: Combined function automation. The vehicle has multiple automation features, but the driver is still required to pay attention to the road.
Example: Tesla’s Autopilot feature.
Level 3: Limited self-driving automation. The vehicle can handle most aspects of driving itself, but the driver must be ready to take over at any time.
Example: Audi’s Traffic Jam Pilot prototype.
Level 4: The vehicle has nearly full autonomous capabilities, but there are still certain circumstances under which the driver must take control.
Example: Level 4 autonomous vehicles are not yet commercially available, but there are a few prototypes, such as the Google Self-Driving Car.
Level 5: Full self-driving automation with no human driver required. The vehicle can handle all aspects of driving itself and does not require a human driver.
Example: There are no Level 5 autonomous vehicles commercially available yet, but that’s the ultimate goal of many companies, including Tesla.
The Many Tasks Of Machine Learning For Autonomous Vehicles
Machine learning is increasingly necessary as autonomous vehicle technology advances. To be fully autonomous, AVs must be capable of a variety of activities, such as spotting and avoiding hazards, adhering to traffic laws, and making decisions in response to changing circumstances. An ML system must deal with the specific challenges that each of these jobs poses.
Engineers must first create a system that can interface and comprehend all of the various ML systems in order to create an AV that is capable of carrying out all of these duties. This is a difficult process because every system is made to perform a certain function and may make use of many data formats and standards. Nevertheless, it is necessary to develop a truly autonomous. The large number of ML activities that must be coupled provides a particular issue. Even if each activity is significant on its own, the real difficulty comes from creating a system that can comprehend and utilise the data from all of the various tasks. This is necessary to build a completely autonomous car.
- Vehicle Localization
- Pedestrian Detection
- Traffic Sign Detection
- Road-marking detection
- Automated Parking
- Self-localization
- Lane Detection
- Driver Assistance Systems
The Advantages Of Autonomous Vehicles
Autonomous vehicles (AVs) have the potential to revolutionize transportation, offering a number of advantages over traditional human-driven vehicles. These include:
- Improved safety: Human error is a factor in over 90% of traffic accidents. AVs are equipped with a variety of sensors that allow them to perceive their surroundings and make decisions much faster than humans can. This could lead to a significant reduction in accidents and fatalities.
- Reduced congestion: AVs can communicate with each other and coordinate their movements to optimize traffic flow. This could help to reduce congestion and improve travel times.
- Increased fuel efficiency: AVs can be programmed to drive more efficiently than humans, avoiding unnecessary acceleration and braking. This could lead to significant fuel savings, both for individuals and for the environment.
- Increased productivity: AVs would free up people’s time to work, read, or relax while they commute. This could lead to increased productivity and economic growth.
- Greater accessibility: AVs could make transportation more accessible for people with disabilities and the elderly. For example, people who are blind or have limited mobility could use AVs to get around without having to rely on others.
Conclusion
In autonomous vehicles, machine learning plays the job of allowing the vehicle to learn from data and make predictions about its surroundings. The behaviour of objects, people, and other cars on the road can be predicted using machine learning techniques. Making judgements about when to brake, turn, or accelerate, as well as normal vehicle steering, can be done using this information.
All of this may hasten the day when we may finally take advantage of the advantages of the age of self-driving cars. Even partially autonomous systems (such Level 1, Level 2, and Level 3) can significantly enhance the driving experience, thus it’s crucial to focus on those issues as well.
References
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Singh, Sehajbir, and Baljit Singh Saini. “Autonomous cars: Recent developments, challenges, and possible solutions.” In IOP Conference Series: Materials Science and Engineering, vol. 1022, no. 1, p. 012028. IOP Publishing, 2021.