EM3: Driver Assistance Systems at Karlsruher Institut für Technologie

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Study with flashcards and summaries for the course EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Name some duties of driver assistance systems

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Describe how a camera is working

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Name advantages and disadvantages of cameras

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Describe how time of flight sensors are working

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Describe properties and working methods of ultrasonic sensors

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Name advantages and disadvantages of ultrasonic sensors

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Describe the properties and working principles of radar sensors

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Name advantages and disadvantages of radar sensors

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Name advantages and disadvantages of lidar sensors

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Name different localisation approaches and their positive and negative aspects

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Common definition of machine learning

Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

Explain principle of supervised learning

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Exemplary flashcards for EM3: Driver Assistance Systems at the Karlsruher Institut für Technologie on StudySmarter:

EM3: Driver Assistance Systems

Name some duties of driver assistance systems

- Minimize driver stress
􏰕

- Eliminate deficit in the perception and processing of relevant driver information


- Claim the driver as little as possible
􏰕

- Do not give false warnings
􏰕

- Act as much as possible as the driver himself (during vehicle interventions)


􏰕- Help to avoid mistakes of the driver
􏰕

- Mitigate the consequences of driving errors that occur nevertheless


- Make traffic more efficient, more economical and more environmentally friendly


EM3: Driver Assistance Systems

Describe how a camera is working

- Operating in visible spectral range of light

- Each photodiode forms a pixel

- The larger the pixel the more light can be captured

- RGB color filters allow reconstruction of color in an image


- Each camera is slightly different ➔ Calibration is important

- Rolling shutter effect can cause issues

EM3: Driver Assistance Systems

Name advantages and disadvantages of cameras

Advantages

- High angular resolution

- Allows color vision (e.g. detection of yellow lines)

- Good for object classification

- Comprehensive data for humans

- Cheap

Disadvantages

- Complex distance estimation with mono cameras

- Susceptible to glare

- Restriction at night, rain, fog, 

- Limited range <100m

- Susceptible to poolution

- Demanding data processing

EM3: Driver Assistance Systems

Describe how time of flight sensors are working

- Emit signal and measure time until echo is received

- Distance calculated based on runtime and speed of wave


Challanges

- Echo may never reach sensor (stealth aircraft)

- Minimum object size and distance limited by physical properties

- Limited resolution

- Many disturbances that must be filtered

EM3: Driver Assistance Systems

Describe properties and working methods of ultrasonic sensors

- Piezo material generates impulses and receives echo

- Triangulation with multiple sensors

- 0.25 - 4m range

- Wide horizontal angle

- Use in parking and low speed use cases

- Wind as disturbance factor

EM3: Driver Assistance Systems

Name advantages and disadvantages of ultrasonic sensors

Advantages

- Insensitive to poor visibility

- Detection largely independent of material and surface

- Insensitive to contamination (self cleaning)

- Small design and highly integrated

Disadvantages

- Limited range

- Limited resolution

- Error due do misdirected reflections possible

- Influenced by other sound sources

- Does not read color

EM3: Driver Assistance Systems

Describe the properties and working principles of radar sensors

- Pulse - echo principle with electromagnetic waves

- Continuous wave (relative speed measurement)

- Frequency modulated continuous wave

- Phased array to control measurement direction

- Angle information in horizontal and vertical plane

- Object distance

- Object speed based on phase shift

EM3: Driver Assistance Systems

Name advantages and disadvantages of radar sensors

Advantages

- Independent of weather and time

- Large sensor range (250m)

- Measurement though housing or below other vehicles

Disadvantages

- Changing reflection centres leading to errors

- Limited angle resolution

- Overlapping with other radar sensors

- No color

- Challenging evaluation

EM3: Driver Assistance Systems

Name advantages and disadvantages of lidar sensors

Advantages

- Very good distance resolution

- Detection of smaller objects

- Exact angular resolution

- Immune to other lidar sensors

- daylight independent

- can determine weather condition

Disadvantages

- Problem with glare

- Surface dependency (absorbant materials)

- Not suitable for bad weather

- Mechanical scanner fragile

- Non mechanical scanner very expensive

EM3: Driver Assistance Systems

Name different localisation approaches and their positive and negative aspects

  • GNSS based localization
    • based on satellite system
    • + Cheap
    • + Fusion allows increased accuracy
    • - Problems in urban canyons
    • - No indoor driving
    • - Low accuracy
  • Map based localization
    • Match sensor data with map data to get current position
    • Fusion over time using probabilistic filters
    • + Very accurate
    • + No stellites
    • - Huge amount of data needed
    • - Localisation needs existing map
    • - Dependant on daytime and weather
  • Simultaneous localization and mapping
    • Map generated on the fly
    • + No prior map needed
    • + No satellites needed
    • - Large amount of data
    • - No additional semantic information in map
    • - Accuracy not given during creation of map
    • - High computational effort

EM3: Driver Assistance Systems

Common definition of machine learning

System learns from experience with respect to a class of tasks and a performance measure when its performance in tasks increases through experience.

It generates one or more solution hypotheses to solve the tasks.

EM3: Driver Assistance Systems

Explain principle of supervised learning

Estimation of the best function parameters (hypothesis) using known examples (training data) generated by a unknown system.

Problem is defined by X x Y:

X ➔ True, false: Concept learning

X ➔ Set of classes: Classification

X ➔ R: Numerical regression


Estimate empirical error of given parameter set based on given sample data. Error calculated based on integrated loss function or probability of incorrect result.

Learn Data ➔ learn error

Verification Data ➔ verification error

Test Data ➔ generalisation error

Independent and identical distributed data sets needed!


Calculate best parameters by minimising error function. Gradient descent is one possible method. Parameters are changed based on the derivative of the error function and a constant learning rate.

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