Search Constraints
Number of results to display per page
Results for:
« Previous |
1 - 10 of 26
|
Next »
Search Results
-
Video
This lecture gives an intuitive explanation of economics concept known as "Nash equilibrium" using traffic signals as an example.
- Course related:
- AMA1D04 Understanding Social Conflicts by Game Theory
- Subjects:
- Economics and Transportation
- Keywords:
- Game theory Traffic signs signals Traffic engineering Equilibrium (Economics)
- Resource Type:
- Video
-
MOOC
In autonomous vehicles such as self-driving cars, we find a number of interesting and challenging decision-making problems. Starting from the autonomous driving of a single vehicle, to the coordination among multiple vehicles. This course will teach you the fundamental mathematical model for many of these real-world problems. Key topics include Markov decision process, reinforcement learning and event-based methods as well as the modelling and solving of decision-making for autonomous systems. This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to develop their knowledge in decision-making models for autonomous systems. Enhance your decision-making skills in automotive engineering by learning from Chalmers, one of the top engineering schools that distinguished through its close collaboration with industry.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Decision making Automobiles -- Design construction Automated vehicles
- Resource Type:
- MOOC
-
MOOC
Electric powertrains are estimated to propel a large part of road vehicles in the future, due to their high efficiency and zero tailpipe emissions. But, the cost and weight of batteries and the time to charge them are arguments for the conventional powertrain in many vehicles. This makes it important for engineers working with vehicles to understand how both these powertrains work, and how to determine their performance and energy consumption for different type of vehicles and different ways of driving vehicles. This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to develop their knowledge about electric powertrains. In this course, you will learn how electric and conventional combustion engine powertrains are built and how they work. You will learn methods to calculate their performance and energy consumption and how to simulate them in different driving cycles. You will also learn about the basic function, the main limits and the losses of: Combustion engines, Transmissions Electric machines, Power electronics Batteries. This knowledge will also be a base for understanding and analysing different types of hybrid vehicles, discussed in the course, Hybrid Vehicles. As a result of support from MathWorks, students will be granted access to MATLAB/Simulink for the duration of the course.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Electric vehicles Automobiles -- Power trains
- Resource Type:
- MOOC
-
MOOC
In this course, we will introduce you to the fundamentals of sensor fusion for automotive systems. Key concepts involve Bayesian statistics and how to recursively estimate parameters of interest using a range of different sensors. The course is designed for students who seek to gain a solid understanding of Bayesian statistics and how to use it to fuse information from different sensors. We emphasize object positioning problems, but the studied techniques are applicable much more generally. The course contains a series of videos, quizzes and hand-on assignments where you get to implement many of the key techniques and build your own sensor fusion toolbox. The course is self-contained, but we highly recommend that you also take the course ChM015x: Multi-target Tracking for Automotive Systems. Together, these courses give you an excellent foundation to tackle advanced problems related to perceiving the traffic situation around an autonomous vehicle using observations from a variety of different sensors, such as, radar, lidar and camera.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Automobiles -- Electronic equipment Automotive sensors
- Resource Type:
- MOOC
-
MOOC
Why are hybrid vehicles still more common than battery electric ones? Why are electric vehicles still more expensive than conventional or hybrid ones? In this course, you will get the answers to this and much more. While electric motors can improve vehicles regarding performance, energy consumption and emissions, they suffer from high cost and weight of batteries. Smart combinations of electric motors and combustion engines in a hybrid powertrain can combine these strengths with the advantages of combustion engines. This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to develop their knowledge about hybridpowertrains. Inthis course, we willexamine different mechanical layouts of hybrid powertrains and how they influence the performance and complexity of the powertrain. Different sizing of powertrains in micro, mild, full hybrids, as well as plug-in hybrids, is also discussed and you'll learn how they can be modelled and analyzed for example by simulation of driving cycles. You will also learn about the Energy Management system and how this controls the hybrid powertrain modes and when to charge and discharge the battery. As a result of support from MathWorks, students will be granted access to MATLAB/Simulink for the duration of the course.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Electric vehicles Hybrid electric vehicles
- Resource Type:
- MOOC
-
MOOC
Autonomous vehicles, such as self-driving cars, rely critically on an accurate perception of their environment. In this course, we will teach you the fundamentals of multi-object tracking for automotive systems. Key components include the description and understanding of common sensors and motion models, principles underlying filters that can handle varying number of objects, and a selection of the main multi-object tracking (MOT) filters. The course builds and expands on concepts and ideas introduced in CHM013x: ""Sensor fusion and nonlinear filtering for automotive systems"". In particular, we study how to localize an unknown number of objects, which implies various interesting challenges. We focus on cameras, laser scanners and radar sensors, which are all commonly used in vehicles, and emphasize on situations where we seek to track nearby pedestrians and vehicles. Still, most of the involved methods are more general and can be used for surveillance or to track, e.g., biological cells, sports athletes or space debris. The course contains a series of videos, quizzes and hands-on assignments where you get to implement several of the most important algorithms. Learn from award-winning and passionate teachers to enhanceyour knowledge at the forefront of research on self-driving vehicles. Chalmers is among the top engineering schools that distinguish itself through its close collaboration with industry.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Automobiles -- Design construction Computer vision Automated vehicles
- Resource Type:
- MOOC
-
Courseware
Life in the city relies on the smooth operation of urban logistics. Everything from retail to services, construction to waste collection rely on an efficient and reliable freight transport system. However, with the increasing pressures of urbanization, this has to be balanced with the environmental and social impacts caused by transport activity. This is the challenge of City Logistics, a field of study that has significant practical implications for the world and the cities we live in. It is not merely a question of what is involved, but what can be done about urban freight transport to improve it for the sake of economic efficiency, quality of life, and sustainability. From a systematic scientific foundation of the field, this course will take you on a journey to learn how city logistics is understood and practiced in cities around the world. Our instructors, members of a renowned global expert network, will teach you the basics of this highly complex social system. Using their experience in real-world projects, they will illustrate how the knowledge learnt in this course is applied across industry and the public sector. This course caters primarily to university students or professionals working in urban transport infrastructure planning or logistics management. Whether you are simply curious about the topic or you intend to develop a career in these fields, this course will give you the tools you need to understand the complexities of urban freight transport systems. The course emphasizes the theoretical foundation, the rigorous evaluation, and a multi-disciplinary approach to this complex area. Course participants will benefit from numerous case studies of best practice in selected cities around the world, in a variety of business settings. Our emphasis on the global perspective is particularly relevant, since an understanding of local culture and political climate is an important factor in the success of any city logistics intervention. The course will provide an avenue for students to learn from their peers about the challenges faced in their respective cities, and how to apply the principles learned to the challenges faced in their own cities.
- Subjects:
- Transportation
- Keywords:
- Freight freightage Transportation -- Environmental aspects Sustainable development Urban transportation
- Resource Type:
- Courseware
-
MOOC
Engineers in the automotive industry are required to understand basic safety concepts. With increasing worldwide efforts to develop connected and self-driving vehicles, traffic safety is facing huge new challenges. This course is for students or professionals who have a bachelor's degree in mechanical engineering or similar and who are interested in a future in the vehicle industry or in road design and traffic engineering. It's also of value for people already working in these areas who wantbetter insight into safety issues. This course teaches the fundamentals of active safety (systems for avoiding crashes or reducing crash consequences) as well as passive safety (systems for avoiding or reducing injuries). Key concepts include in-crash protective systems, collision avoidance, and safe automated driving. The course will introduce scientific and engineering methodologies that are used in the development and assessment of traffic safety and vehicle safety. This includes methods to study the different components of real-world traffic systems with the goal to identify and understand safety problems and hazards. It includes methods to investigate the attitudes and behavior of drivers and other road users as well as recent solutions to improve active safety. Italso includes methods to study human body tolerance to impact and solutions to minimize the injury risk in crashes. Study topics include crash data analysis and in-situ observational studies of drivers and other road users by the use of instrumented vehicles and roadside camera systems. Solutions in active safety, such as driver alertness monitoring, driver information as well as collision avoidance and collision mitigation systems, will be described. Examples of in-crash protective systems are combinations of traditional restraints such as seat belts and airbags but with advanced functions such as automatic adaption to the individual occupant as well as pre-collision activation based on advanced integrated sensor systems and communication systems. The course will be based on recorded lectures that use videos and animations to enhance the experience. Online tutorials that access simulation models will give the participants an experience of influencing parameters in active safety and passive safety systems. As a result of support from MathWorks, students will be granted access to MATLAB/Simulink for the duration of the course.
- Subjects:
- Transportation
- Keywords:
- Traffic safety Roads -- Design construction Motor vehicles -- Safety measures Automobile industry trade
- Resource Type:
- MOOC
-
Courseware
1. Objectives of modelling in transport and spatial planning. Model types. Theory of travel and locational behaviour. System description of planning area. Theory of choice models. Aggregate and disaggregate models. Mode choice, route choice and assignment modelling. Locational choice modelling. Parameter estimation and model calibration. Cases and exercises in model application; 2. Role of models in transportation and spatial systems analysis; model types; designing system description of study area (zonal segmentation, network selection); role of shortest path trees; 3. Utility theory for travel and location choice; trip generation models, trip distribution models; applications; 4. Theory of spatial interaction model; role of side constraints; distribution functions and their estimations; constructing base matrices and estimating OD-tables; 5. Theory of individual choice models; 6. Disaggregated choice models of the logit and probit type for time choice, mode choice, route choice and location choice; 7. Integrated models (sequential and simultaneous) for constructing OD-tables; 8. Equilibrium theory in networks and spatial systems; 9. Route choice and assignment; derivation of different model types (all-or-nothing model, multiple route model, (stochastic) equilibrium model); assignment in public transportation networks; analyses of effects; 10. Calibration of parameters and model validation; observation, estimation, validation; estimation methods; 11. Individual exercise computing travel demand in networks; getting familiar with software; computing all transportation modelling steps; analyse own planning scenarios; writing a report.
- Subjects:
- Transportation
- Keywords:
- Spatial systems Transportation
- Resource Type:
- Courseware
-
Courseware
This course discusses fundamental traffic flow characteristics and traffic flow variables. Their definitions are presented, and visualization/analysis techniques are discussed and empirical facts are presented. The empirical relation between the flow variables and the bottleneck capacity analysis are discussed. Shockwave analysis and a review of macroscopic traffic flow models are presented. Traffic flow stability issues are discussed as well as numerical solution approaches. The lectures also show how macroscopic models are derived from microscopic principles. This course provides an overview of human factors relevant for the behavior of drivers. The car-following model and other approaches to describe the lateral driving task will be discussed. The lectures also pertains to general gap acceptance modeling and lane changing. Microscopic models for pedestrian flow behavior are discussed and an in depth discussion of microscopic simulation models will be presented.
- Subjects:
- Transportation
- Keywords:
- Traffic flow -- Simulation methods Traffic engineering Traffic flow -- Mathematical models Traffic flow
- Resource Type:
- Courseware
- « Previous
- Next »
- 1
- 2
- 3