CS-7638 Midterm Exam and Final Exam |
All Questions and Correct Answers |
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CS-7638 Midterm Exam
What is the primary function of the laser-range finder in a self-driving car? ---------
CORRECT ANSWER-----------------To take distance scans 10 times a second and
collect about a million data points to spot other cars.
What additional equipment is used in self-driving cars for localization? ---------
CORRECT ANSWER-----------------A stereo camera system and GPS antennas.
What is the main purpose of tracking in self-driving cars? ---------CORRECT
ANSWER-----------------To understand the position and speed of other vehicles to
avoid collisions.
How do Kalman filters differ from Monte Carlo localization? ---------CORRECT
ANSWER-----------------Kalman filters estimate a continuous state, while Monte
Carlo localization uses discrete places.
,What type of distribution do Kalman filters produce? ---------CORRECT ANSWER----
-------------A unimodal distribution.
What is the goal of using a Kalman filter in tracking? ---------CORRECT ANSWER-----
------------To estimate future locations and velocities based on noisy and uncertain
data.
What is a Gaussian in the context of Kalman filters? ---------CORRECT ANSWER------
-----------A continuous function characterized by a mean (μ) and variance (σ²) that
represents the probability distribution of the state.
What does the area under a Gaussian curve represent? ---------CORRECT ANSWER-
----------------It sums up to 1, indicating the total probability.
What are the two parameters that characterize a Gaussian? ---------CORRECT
ANSWER-----------------The mean (μ) and the variance (σ²).
What does a larger variance (σ²) indicate about a distribution? ---------CORRECT
ANSWER-----------------It indicates greater uncertainty about the actual state.
,What is the relationship between covariance and the spread of a Gaussian
function? ---------CORRECT ANSWER-----------------Larger covariance results in a
wider spread of the function.
What does the term 'unimodal' refer to in Gaussian distributions? ---------
CORRECT ANSWER-----------------It refers to distributions that have a single peak.
What is the significance of the quadratic function in the Gaussian formula? ---------
CORRECT ANSWER-----------------It helps to determine the shape of the Gaussian
distribution based on the distance from the mean.
What is the expected output of the Kalman filter when given noisy
measurements? ---------CORRECT ANSWER-----------------An estimate of future
locations and velocities that accounts for uncertainty.
What is the purpose of normalization in the Gaussian formula? ---------CORRECT
ANSWER-----------------To ensure that the area under the curve sums to 1.
What is the main focus of the Kalman filter class mentioned in the notes? ---------
CORRECT ANSWER-----------------To teach how to write software that estimates
future locations and velocities using sensor data.
, What is the role of the Google self-driving car in the context of Kalman filters? -----
----CORRECT ANSWER-----------------It uses methods like Kalman filters to
understand the position of other traffic based on radar and laser-range data.
How does the Kalman filter handle uncertainty in measurements? ---------
CORRECT ANSWER-----------------By maintaining estimates of the mean and
variance to represent the state of the system.
What is the significance of the exponential function in the Gaussian distribution? -
--------CORRECT ANSWER-----------------It describes how the probability decreases
as you move away from the mean.
What is the expected behavior of an object moving with constant velocity in a
Kalman filter? ---------CORRECT ANSWER-----------------The filter predicts future
positions based on past measurements and assumed constant velocity.
How does the Kalman filter improve over time? ---------CORRECT ANSWER-----------
------By continuously updating its estimates based on new measurements.
What is the relationship between the Kalman filter and particle filters? ---------
CORRECT ANSWER-----------------Both are techniques for estimating state, but
particle filters can handle multimodal distributions.
All Questions and Correct Answers |
Verified Answers | Brand New Version!
CS-7638 Midterm Exam
What is the primary function of the laser-range finder in a self-driving car? ---------
CORRECT ANSWER-----------------To take distance scans 10 times a second and
collect about a million data points to spot other cars.
What additional equipment is used in self-driving cars for localization? ---------
CORRECT ANSWER-----------------A stereo camera system and GPS antennas.
What is the main purpose of tracking in self-driving cars? ---------CORRECT
ANSWER-----------------To understand the position and speed of other vehicles to
avoid collisions.
How do Kalman filters differ from Monte Carlo localization? ---------CORRECT
ANSWER-----------------Kalman filters estimate a continuous state, while Monte
Carlo localization uses discrete places.
,What type of distribution do Kalman filters produce? ---------CORRECT ANSWER----
-------------A unimodal distribution.
What is the goal of using a Kalman filter in tracking? ---------CORRECT ANSWER-----
------------To estimate future locations and velocities based on noisy and uncertain
data.
What is a Gaussian in the context of Kalman filters? ---------CORRECT ANSWER------
-----------A continuous function characterized by a mean (μ) and variance (σ²) that
represents the probability distribution of the state.
What does the area under a Gaussian curve represent? ---------CORRECT ANSWER-
----------------It sums up to 1, indicating the total probability.
What are the two parameters that characterize a Gaussian? ---------CORRECT
ANSWER-----------------The mean (μ) and the variance (σ²).
What does a larger variance (σ²) indicate about a distribution? ---------CORRECT
ANSWER-----------------It indicates greater uncertainty about the actual state.
,What is the relationship between covariance and the spread of a Gaussian
function? ---------CORRECT ANSWER-----------------Larger covariance results in a
wider spread of the function.
What does the term 'unimodal' refer to in Gaussian distributions? ---------
CORRECT ANSWER-----------------It refers to distributions that have a single peak.
What is the significance of the quadratic function in the Gaussian formula? ---------
CORRECT ANSWER-----------------It helps to determine the shape of the Gaussian
distribution based on the distance from the mean.
What is the expected output of the Kalman filter when given noisy
measurements? ---------CORRECT ANSWER-----------------An estimate of future
locations and velocities that accounts for uncertainty.
What is the purpose of normalization in the Gaussian formula? ---------CORRECT
ANSWER-----------------To ensure that the area under the curve sums to 1.
What is the main focus of the Kalman filter class mentioned in the notes? ---------
CORRECT ANSWER-----------------To teach how to write software that estimates
future locations and velocities using sensor data.
, What is the role of the Google self-driving car in the context of Kalman filters? -----
----CORRECT ANSWER-----------------It uses methods like Kalman filters to
understand the position of other traffic based on radar and laser-range data.
How does the Kalman filter handle uncertainty in measurements? ---------
CORRECT ANSWER-----------------By maintaining estimates of the mean and
variance to represent the state of the system.
What is the significance of the exponential function in the Gaussian distribution? -
--------CORRECT ANSWER-----------------It describes how the probability decreases
as you move away from the mean.
What is the expected behavior of an object moving with constant velocity in a
Kalman filter? ---------CORRECT ANSWER-----------------The filter predicts future
positions based on past measurements and assumed constant velocity.
How does the Kalman filter improve over time? ---------CORRECT ANSWER-----------
------By continuously updating its estimates based on new measurements.
What is the relationship between the Kalman filter and particle filters? ---------
CORRECT ANSWER-----------------Both are techniques for estimating state, but
particle filters can handle multimodal distributions.
