Length: 3 Days
Autonomous Driving Fundamentals Training by Tonex
Tonex offers Autonomous Driving Fundamentals Training, equipping participants with essential knowledge in self-driving technology. This comprehensive program covers sensors, AI algorithms, safety, and regulatory aspects. Participants gain hands-on experience to excel in the dynamic field of autonomous vehicles, preparing them for the future of transportation innovation.
Though still in its infancy, autonomous driving technology is surging. The impact autonomous vehicles will eventually have on society is unknown, but it will surely be significant.
A self-driving car is capable of sensing its environment and navigating without human input. It does this using a technology called Sensor Fusion, a data analysis technique that combines pieces of information coming from different sources or sensors in order to arrive at the best decisions.
To accomplish this task, each vehicle is usually outfitted with a GPS unit, an inertial navigation system, and a range of sensors including laser rangefinders, radar and video. The vehicle uses positional information from the GPS and inertial navigation system to localize itself and sensor data to refine its position estimate as well as to build a three-dimensional image of its environment.
The majority of self-driving vehicle control systems implement a deliberative architecture, meaning that they are capable of making intelligent decisions by 1) maintaining an internal map of their world and 2) using that map to find an optimal path to their destination that avoids obstacles (e.g., road structures, pedestrians and other vehicles) from a set of possible paths.
Once the vehicle determines the best path to take, the decision is dissected into commands, which are fed to the vehicle’s actuators. These actuators control the vehicle’s steering, braking and throttle.
Another important concept to understand about autonomous vehicles is they are being developed using deep learning methodology and neural networks. A neural network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain.
Essentially, a neural network works on a system of probability based on data fed to it. It is able to make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables “learning” by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future.
While discussions pursue on how autonomous driving should be regulated, the layers of autonomy are generally agreed upon as this:
Level 0: All major systems are controlled by humans.
Level 1: Certain systems, such as cruise control or automatic braking, may be controlled by the car, one at a time.
Level 2: The car offers at least two simultaneous automated functions, like acceleration and steering, but requires humans for safe operation.
Level 3: The car can manage all safety-critical functions under certain conditions, but the driver is expected to take over when alerted.
Level 4: The car is fully-autonomous in some driving scenarios, though not all
Level 5: The car is completely capable of self-driving in every situation.
Projected benefits of autonomous driving include:
- Safety – Accidents now caused by human error should be substantially reduced.
- Less Traffic – More efficient traffic flow could eliminate urban congestion all together.
- Costs – Safer driving should reduce insurance costs and better efficiency should lower fuel costs.
- Quality of life – Autonomous cars could reduce behind the wheel commuting by many hours, leaving more time for leisure. Those who can’t drive or shouldn’t drive would also benefit.
- Miscellaneous – The autonomous driving revolution is anticipated to dramatically increase the use of mass transit and other transportation services such as self-driving taxis. With fewer individually owned cars needing parking, this could free up space for bike lanes and allow parking lot areas to be used for other purposes.
Course Content
Introduction to Autonomous Driving Fundamentals
- What is Autonomous Driving?
- Autonomous Driving defined
- Structured and unstructured data
- Data and analytics
- Role of neural networks
- Sensor fusion and other technologies involved
- Understanding deliberative architecture
- Importance of software
- Levels of autonomy
- Benefits of Autonomous Driving
- Possible problems
- Impact on society
- History of Autonomous Driving
- The need for regulations
Autonomous Driving Technologies
- Systems and infrastructure
- Neural networking
- Security
- Sensor fusion
- Actuators
- Feedback loops
- Deliberative architecture
- Policies
- Laser rangefinders, radar and video
- GPS and inertial navigation systems
Who Should Attend?
Anyone impacted by the Autonomous Driving revolution including electronic company personnel, computer science specialists, niche vehicle manufacturers, companies or individuals exploring the market with some knowledge of relevant technologies, car manufactures / OEMs, software and electrical engineers, students in physics, robotics, automotive engineering, automation, product design from undergraduate to Ph.D. level.
Why Choose Tonex?
–Presenting highly customized learning solutions is what we do. For over 30 years Tonex has worked with organizations in improving their understanding and capabilities in topics often with new development, design, optimization, regulations and compliances that, frankly, can be difficult to comprehend.
–Ratings tabulated from student feedback post-course evaluations show an amazing 98 percent satisfaction score.
–Reasonably priced classes taught by the best trainers is the reason all kinds of organizations from Fortune 500 companies to government’s most important agencies return for updates in courses and hands-on workshops
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