Coursera Automobile Engineering Courses & Certifications

Coursera Introduction to Self-Driving Cars online course introduces you to the concept of self-driving cars. You will be learning the details about the hardware used in self-driving cars, components of a self-driving software stack, and how to program vehicle control and modelling. You will also analyse the current industry practices and safety structure related to vehicle development.

The Introduction to Self-Driving Cars programme by Coursera is offered in collaboration with the University of Toronto. You will learn to develop the control code that will help in navigating a self-driving car in a racetrack with the CARLA simulation environment as part of your final project. Certain hardware and software specifications are required to be able to run the CARLA simulator, which will be covered in the curriculum.

Furthermore, the Introduction to Self-Driving Cars courses houses several practice exercises, quizzes, graded assignments, and a final project. All these elements will help you learn and master the core concepts covered during the programme and reinforce your learning. Besides, upon successful completion of the course, you become eligible for the award of a course completion certificate by Coursera. 

The course Motion Planning for Self-Driving Cars is the final course among four courses in Self-driving cars specialisation offered by the University of Toronto. The level of the course is advanced and is designed for the learners having a background of robotics and knowledge of controllers and models taught in course 1 of the specialisation.

In the course, the participant will be introduced to major planning related tasks in an autonomous mode of driving along with various other types of planning like behaviour planning, local planning, and mission planning. At the end of the programme, the participant will be able to find the shortest and direct path over a road network or a graph by making the use of A* algorithm and Dijkstra’s. They will be using machines in the finite state for selection of smooth paths, optimal designs, and identification of velocity profiles required for the navigation around the obstacles with safety and in accordance to the traffic rules. The participants will also get a chance to develop occupancy grid maps for the static objects prevailing in the environment and use them to check efficient collision.

The course will provide the opportunity to develop a complete self-driving planning solution that will provide a contemporary driving experience as well as safety while taking an individual from home to work. As a part of the final project, the candidate will be implementing the planner of hierarchical motion by navigating through the sequence of scenarios in the CARLA simulator, safe navigation during intersection, and avoiding the already parked vehicles in the lane.

Coursera’s Battery State-of-Charge (SOC) Estimation course is the third part of the five-course Algorithms for Battery Management Systems Specialisation. It can also be taken as an academic credit course as ECEA 5732, which is part of the University of Columbia Boulder’s Master of Science in Electrical Engineering degree.

The Battery State-of-Charge (SOC) Estimation online programme trains you about the implementation of various state-of-charge estimation methods. It teaches you how to evaluate their corresponding merits for lithium-ion battery cells. You will be introduced to the sequential-probabilistic-inference solution and the Octave/MATLAB script for linear Kalman filter while also evaluating the results.

The intermediate-level Battery State-of-Charge (SOC) Estimation course by Coursera is offered by the University of Colombia Boulder and the University of Colombia Systems, which are well-known globally for their standard of education. The instructor for this course will be Professor Gregory Plett, who teaches Electrical and Computer engineering. At the end of the Battery State-of-Charge (SOC) Estimation course, you will work on the Capstone project, which will help you apply all the skills you have learned practically.

Electric Engineering is one of the best course studies to be a part of and it is one that has a definite position in both present and future, offering stable career opportunities for a long time to come. A certificate of this course from a university as prestigious as the University of Colorado Boulder and the University of Colorado System is guaranteed to make many doors and windows open through.

The Equivalent Circuit Cell Model Modulation programme by Coursera has been brought to the candidates with excellent content materials and real world based projects for an intermediate level. It teaches the values, properties, and simulation of each of the components of a lithium Ion cell and hones these skills to perfection.

Not only that, the experience gained here along with the course studied in such great detail will put the candidates at the top bar for Electronic Engineers and give them an early and the very important head start in the competition of today.

The Visual Perception for Self-Driving Cars certification course will introduce candidates to the key perception tasks and survey popular computer vision methods for robotic perception in autonomous driving, and dynamic and static object detection. Candidates will acquire the skills to work with the pinhole camera model by the end of this course. Along with them they will detect, explain and fit image characteristics, conduct intrinsic and extrinsic calibration of the camera and build their own convolutional neural networks. For drivable surfaces, estimation candidates can apply these methods to object detection and tracking, visual odometry, and semantic segmentation.

Candidates will build algorithms for the final project in the Visual Perception for Self-Driving Cars training course that defines the limits of the drivable surface and recognise bounding boxes for objects in the scene. On a realistic dataset, students learn to work using synthetic as well as real image data. 

The Visual Perception for Self-Driving Cars online course is part of a self-driving car specialisation programme. It is the third one of a total 4 courses. This specialisation gives a detailed understanding of state-of-the-art engineering approaches used in the self-driving automotive industry. 

The purpose of the Coursera Electric Vehicles and Mobility online course is to assist you in acquiring the concepts from a wide array of educational fields such as engineering science, sociology, political science, economics, and management science, among others.

The Electric Vehicles and Mobility training by Coursera will help you learn about the impact of the present transportation systems in France, Europe and worldwide as well. The programme also covers the concept of fuel poverty, electric vehicles, the global dependence on oil, and the technical processes impacting global mobility. Besides, the course is self-paced, which allows you to learn at your convenience.

The Electric Vehicles and Mobility training programme is a MOOC with French as the language of delivery. However, the course presentations are in English with English subtitles. The course is offered by École des Ponts ParisTech. The instructors are industry-experts who boast relevant experience in the discipline. The course curriculum covers all the essential concepts to help you gain a comprehensive understanding.

The Introduction to Battery-Management Systems programme by Coursera is the first course in Algorithms for Battery Management Systems Specialization. This Specialization comprises five courses devoted to requirements and understanding of the Battery Management systems. This course takes approximately 24 hours to finish, and the teaching medium is English.

The University of Colorado Boulder and the University of Colorado System jointly offer the Introduction to Battery-Management Systems online course. The course instructor is Professor Gregory Plett, who is a faculty in the University's Department of Electrical and Computer Engineering. The concepts are explained clearly with a unique teaching style, making the subject more compelling.

Furthermore, the Introduction to Battery-Management Systems programme starts from a basic level and progresses to develop a deep understanding of lithium-ion cells and battery management systems. The certification course has a broad curriculum spread over five weeks of lectures and activities. Students are awarded a certificate when they successfully complete the course.

Coursera offers the Estimation and Localization for Self-Driving Cars certification course in association with the University of Toronto. The certification course introduces students to different sensors for correctly estimating the state and localization of a self-driving vehicle.

Moreover, by the end of the Estimation and Localization for Self-Driving Cars course, you will learn all about Kalman Filters and Iterative Closest Point algorithm with LIDAR. The certification course will also cover least squares, and how to relate GPS with IMUs. You will be able to build models for typical vehicle localization sensors.

Upon course completion, you will become adept in developing a full vehicle state estimator independently. The course material is comprehensive and features quizzes, as well as projects, to assist you in learning with ease.

Lastly, candidates will undertake a final project to complete the Coursera State Estimation and Localization for Self-Driving Cars course. On successful completion, candidates will receive a certificate from the University of Toronto, sharable on LinkedIn profiles or CV.