# Motion Planning for Self Driving Cars Quiz

Enroll Now

## Week- 1

### Module 1 Graded Quiz

1.
Question 1
Which are examples of common scenarios in the autonomous driving motion planning problem?

1 point

• A. Left and right turns
• B. Driving up a hill
• C. Lane changes
• D. A & C

2.
Question 2
What are some examples of dynamic obstacles?

1 point

• A. Trees
• B. Cyclists
• C. Cars
• D. Boulevards
• E. B & C

3.
Question 3
True or false, the autonomous driving mission takes pedestrian behaviour into consideration.

1 point

• True
• False

4.
Question 4
True or false, “Staying Stopped” is a maneuver that is useful for handling traffic light controlled intersections.

1 point

• True
• False

5.
Question 5
Which of these are reasons for decomposing motion planning into a hierarchy of optimization problems?

1 point

• A. More computationally efficient
• B. Can tailor each optimization problem to specific level of abstraction
• C. Generates higher-quality solutions than solving the problem in its entirety
• D. None of the above

6.
Question 6
True or false, instantaneous curvature is the inverse of the instantaneous turning radius at a point on a curve.

1 point

• True
• False

7.
Question 7
Static obstacles constrain…

1 point

• The car’s longitudinal velocity
• The locations the car can occupy
• The turning radius of the car
• The car’s lateral velocity

8.
Question 8
A leading vehicle in the ego vehicle’s lane constrains…

1 point

• The car’s longitudinal velocity
• The car’s lateral velocity
• The turning radius of the car
• The car’s maximum jerk

9.
Question 9
True or false, the time gap is the amount of time that it would take for the ego vehicle to reach the current position of its leading vehicle.

1 point

• True
• False

10.
Question 10
True or false, the friction ellipse is always a tighter constraint than the comfort rectangle.

1 point

• True
• False

11.
Question 11
To generate the shortest path to a point, we need to minimize…

1 point

• Curvature
• Arc length
• Angular velocity

12.
Question 12
The integral of difference (IOD) term in a planning objective function can be used to…

1 point

• A. Improve path smoothness
• B. Track a reference velocity profile
• C. Track a reference path
• D. B & C

13.
Question 13
True or false, jerk is the derivative of acceleration with respect to time.

1 point

• True
• False

14.
Question 14
True or false, maximizing jerk increases the comfort of our planned trajectory.

1 point

• True
• False

15.
Question 15
The _____ at each point in the path constrains the velocity that can be driven at that point, due to the lateral acceleration constraints.

1 point

• X position
• Curvature
• Y position
• Heading

16.
Question 16
True or false, mission planning focuses on map-level navigation from the ego vehicle’s current position to a final destination.

1 point

• True
• False

17.
Question 17
What are some examples of the inputs a finite state machine might take in the context of behaviour planning for autonomous driving?

1 point

• Vehicle positions
• Pedestrian locations
• The number of passengers in the ego vehicle
• Traffic light transitions

18.
Question 18
True or false, reinforcement learning relies on interacting with an environment during the learning process.

1 point

• True
• False

19.
Question 19
What is a drawback of using a sampling-based method for path planning?

1 point

• A. If run for a minimal number of iterations, it can generate poor quality paths
• B. It is often slow at exploring the workspace compared to other methods
• C. Sampling based methods are often computationally intractable
• D. None of the above

20.
Question 20
True or false, a conformal lattice planner selects goal points ahead of the car that are laterally offset from the centerline of the road, plans paths to each goal point, then selects the best collision-free path according to some objective function.

1 point

• True
• False

Download

## Week- 3

### Module 3 Graded Quiz

1.
Question 1
Which best describes the mission underlying an autonomous vehicle’s mission planner?

1 point

Generate an optimal, collision-free path to the required destination

Navigate a road network to the desired destination from the ego vehicle’s position

Compute the optimal vehicle behaviour for a given driving scenario

2.
Question 2
Which of these are examples of good values to optimize in a mission planner’s objective function?

1 point

Deviation from the speed limit

Distance from obstacles

Distance travelled

Time to destination

3.
Question 3
What is a graph in the mission planning context?

1 point

A plot of the car’s throttle and steering actuation while executing a driving mission

A chart of the different speeds reached during different road segments in a road network

A discrete mathematical structure used for representing the road network

None of the above

4.
Question 4
True or false, Breadth-First Search (BFS) will explore the graph using a “last-in-first-out” data structure known as a stack.

1 point

True

False

5.
Question 5
True or false, Breadth-First Search (BFS) will always find the optimal (shortest) path in an unweighted graph.

1 point

True

False

6.
Question 6
True or false, Breadth-First Search (BFS) will always find the optimal (shortest) path in a weighted graph.

1 point

True

False

7.
Question 7
In these graph search algorithms, what is the main purpose of keeping track of a “closed” set of graph vertices?

1 point

It allows us to know how much of the graph has been searched

It allows us to avoid getting stuck in cycles

It helps us keep track of which vertices we still need to search

8.
Question 8
What is a min heap data structure?

1 point

A block of memory useful for dynamic memory allocation

A data structure that stores keys and values, and sorts the keys in terms of their associated values, from smallest to largest.

A sorted list of autonomous driving mission priorities for a given driving scenario

A data structure that stores keys and values, and sorts the keys in
terms of their associated values, from largest to smallest.

9.
Question 9
True or false, in a min heap, the root of the heap (the first element) contains the node with the smallest value.

1 point

True

False

10.
Question 10
In Dijkstra’s algorithm, suppose during the process of adding vertices to the open set, we come across a vertex that has already been added to the open set. However, this time we have found a lower cost to reach this vertex than is presently stored in the open set’s min heap. What should be done?

1 point

Update the cost of that vertex in the open set’s min heap

Nothing, as this is impossible under Dijkstra’s algorithm

Nothing, as the vertex is already in the open set

Close the vertex, as we have now seen it twice during exploration

11.
Question 11
What is a search heuristic in the context of mission planning?

1 point

Something that helps the autonomous vehicle efficiently change the autonomus driving mission depending on the situation

A tool that autonomous vehicle’s use for quickly identifying traffic congestion at a given intersection

An estimate of the remaining cost to reach the destination

A method that allows the autonomous vehicle to quickly identify obstacles in its surroundings

12.
Question 12
Suppose I have a vertex at location (2.0, 3.0) and another at location (4.0, 5.0). What is the Euclidean distance between these two points (to three decimal places)?

1 point
Enter answer here

 2.828

13.
Question 13
True or false, an admissable heuristic to the A* search algorithm will never underestimate the cost to reach the goal vertex.

1 point

True

False

14.
Question 14
Is the heuristic function h(v) = 0 an admissible heuristic?

1 point

No, as in this case A* degenerates into BFS

Yes, and in this case A* degenerates into BFS

No, as in this case A* degenerates into Dijkstra’s

Yes, and in this case A* degenerates in DIjkstra’s

15.
Question 15
True or false, the min heap in A* contains the sum of the cost to reach each vertex plus the estimate of the cost to reach the destination from said vertex, according to the search heuristic.

1 point

True

False

## Week- 4

### Module 4 Graded Quiz

1.
Question 1
Which of the following best describes an example of a maneuver-based prediction assumption for motion prediction?

1 point

The behaviour of other agents on the road reduces the space of potential actions

Certain vehicle models restrict vehicle maneuverability, reducing the prediction space

The kinodynamic constraints on a vehicle restricts its potential set of motions

The operating domain of a vehicle restricts the number of feasible or probable maneuvers it can take

2.
Question 2
Which of the following best describes an example of an interactions-aware prediction assumption for motion prediction?

1 point

The behaviour of other agents on the road reduces the space of potential actions

The operating domain of a vehicle restricts the number of feasible or probable maneuvers it can take

Engine dynamics are affected by pedestrian motion, restricing the space of potential actions

The kinodynamic constraints on a vehicle restricts its potential set of motions

3.
Question 3
Which of the following are aspects of pedestrian motion?

1 point

They often have designated lanes on roads due to their slower speed

Potential to leave designated areas unpredictably

High top speed, but must obey the rules of the road

Low top speed, but rapid changes in direction and speed are possible

4.
Question 4
Which of the following are scenarios for which constant velocity estimation provides a useful estimate?

1 point

Roundabouts

Straight roads

Turns and curved roads

Traffic light controlled intersections

5.
Question 5
Which of the following are issues with constant velocity prediction?

1 point

Ignores regulatory elements

Ignores the shape of the road

Doesn’t fully account for vehicle kinodynamics

Computationally expensive

6.
Question 6
Which of the following are position-based assumptions for map-aware prediction algorithms?

1 point

Stop signs will cause vehicles to decelerate to a complete stop

Lane changes can be predicted based on the state of the blinker light of a vehicle

A high-curvature road segment necessitates a slower vehicle speed

Vehicles driving down a lane are likely to follow that lane

7.
Question 7
Which of the following are velocity-based assumptions for map-aware prediction algorithms?

1 point

Stop signs will cause vehicles to decelerate to a complete stop

Lane markings enforce constraints on the location of vehicles in the road

A high-curvature road segment necessitates a slower vehicle speed

A yellow light will cause vehicles to reduce their velocity as they approach an intersection

8.
Question 8
True or false, the more constraints added to our prediction model, the less generalizable it is to all possible traffic scenarios.

1 point

True

False

9.
Question 9
True or false, in the case of the multi-hypothesis prediction approach, the most likely nominal behaviour of a dynamic obstacle based on its state, appearance, and track information is taken as the object’s predicted motion.

1 point

True

False

10.
Question 10
Which of the following are properties of multi-hypothesis prediction approaches?

1 point

Can result in ambiguous predictions

Offers alternative predictions, allowing for fast replanning in case new information arises

Provides a probability distribution over nominal predictions based on the state of the environment.

Provides a maximum likelihood estimate based on the information present in the current traffic scenario

11.
Question 11
At a high level, what best describes the two fundamental steps in computing time to collision?

1 point

Estimating the first vehicle position, then estimating the other vehicle’s velocity

Running trajectory rollout to generate potential paths, then checking each path for intersection points

Compute the location of a collision point along the predicted paths of the dynamic objects, then compute the amount of time to reach said collision point

None of the above

12.
Question 12
True or false, the simulation based approach propogates the movement of every vehicle in the scene over a given time horizon into the future, where the state is computed at multiple time steps along the horizon.

1 point

True

False

13.
Question 13
In estimation-based approaches, which of the following are some of the common simplifying assumptions used in the swath intersection computation?

1 point

Assuming the objects ignore regulatory elements

Assuming a constant speed profile along an object’s predicted path

Identifying collision points based on path intersection points

Estimating spatial occupancy using simple geometric primitives

14.
Question 14
Suppose two vehicles are approximated with a single circle each. The center of one circle is at (1.0 m, 3.0 m) and the other is at (4.0 m, 2.0 m). If the radius of both collision checking circles is 1.5 m, will a collision be detected?

1 point

Yes

No

15.
Question 15
Suppose two vehicles, a leading vehicle and a following vehicle, are moving along a straight line. The center of the leading vehicle is 20 m ahead of the center of the following vehicle. The leading vehicle is moving at 15 m/s, and the following vehicle is moving at 20 m/s. The distance from the center to the front bumper of both vehicles is 2.5 m, and the distance from the center to the rear bumper of both vehicles is 2.5 m. What is the time to collision in this scenario?

1 point
Enter answer here

 3

## Week- 5

### Module 5 Graded Quiz

1.
Question 1
True or false, behavioural planning does not need to take dynamic obstacles into consideration, as it is too low level and should be handled by the local planner.

1 point

True

False

2.
Question 2
As an autonomous vehicle approaches an intersection, which of the following best describes the role of a behavioural planner?

1 point

Plan when and where to stop, how long to stay stopped for, and when to proceed through the intersection

Navigate through the map to find the most efficient path to the required destination.

Determine the throttle angle, brake, and steering angle required to track the reference path through the intersection

Plan a path to the required goal state subject to static/dynamic obstacles and kinodynamic constraints

3.
Question 3
What is the primary output of a behavioural planning module?

1 point

The driving maneuver to be executed in the current environment

A sequence of waypoints that correspond to a feasible, collision-free trajectory

The throttle, brake, and steering angle values required for tracking the reference trajectory

The sequence of road segments to be traversed to reach the destination

4.
Question 4
Which of the following are common inputs to the behavioural planner?

1 point

A mission plan

A default path in the current lane to follow

Localization information

High definition roadmap

5.
Question 5
Which of the following are a disadvantage of using finite state machines for behavioural planning?

1 point

As the number of states decreases, it becomes more computationally complex to evaluate state transitions

As the number of states increases, it becomes increasingly complicated to define all possible transition conditions

Finite state machines can only handle uncertainty when there are many states available

None of the above

6.
Question 6
Which portion of the intersection best describes when the ego vehicle is on the intersection?

1 point

The interior of the intersection

The lane exiting the intersection

The lane preceding the intersection

None of the above

7.
Question 7
Which of the following can increase the size of the “approaching”, “at”, and “on” zones of an intersection?

1 point

The size of the intersection

The size of the ego vehicle

The speed of the ego vehicle

The number of dynamic obstacles present

8.
Question 8
For a 2-lane, 4-way intersection, which of the following maneuvers are absolutely required?

1 point

Track speed

Merge to lane

Stop

Decelerate to stop

9.
Question 9
For this question, let us use our finite state machine discussed in Module 5 Lesson 2. Suppose the car has entered the “Stop” state while at the intersection. Which of the following is the correct transition condition for the vehicle to enter the “Track Speed” state?

1 point

Ego.Position == Approaching

Ego.StopTime >= 3.0 sec

Ego.StopTime < 3.0 sec

Ego.Velocity >= 0

10.
Question 10
For this question, let us use our finite state machine discussed in Module 5 Lesson 2. Suppose the car has entered the “Track speed” state before reaching any zone of the intersection. Which of the following is the correct transition condition for the vehicle to enter the “Decelerate to Stop” state?

1 point

Ego.Position != Approaching

Ego.Position == Approaching

Ego.StopTime < 3.0 sec

Ego.Velocity >= 0

11.
Question 11
Which of the following are the key aspects of dynamic objects that we focus upon in behavioural planning?

1 point

Distance to collision point

Distance to dynamic object

Maximum velocity

Time to collision

12.
Question 12
Which of the following best describes the “Follow Leader” maneuver?

1 point

In a safe and comfortable manner, decelerate to a complete stop to avoid the leading vehicle

When a lead vehicle is performing a lane change, we wait until it is safe and follow them into the adjacent lane

Accelerate to the speed of the lead vehicle, passing the lead vehicle if they are below our reference speed

Follow the speed of, and maintain a safe distance from the lead vehicle

13.
Question 13
True or false, using the state machine developed in L3, when the ego vehicle is in the “Stop” state when in the presence of dynamic obstacles, it should transition to the “Track Speed” state after 3 seconds have elapsed.

1 point

True

False

14.
Question 14
True or false, using the state machine developed in L3, suppose the ego vehicle is “at” the intersection, and is currently in the “Stop” state and 3 seconds have elapsed. Suppose the only dynamic obstacle is “on” the intersection has a heading of 180 degrees relative to the ego heading, and suppose the ego vehicle intends to drive straight. Which state will the state machine transition to?

1 point

Stop

Follow Leader

Track Speed

Decelerate to Stop

15.
Question 15
True or false, using the state machine developed in L3, suppose the ego vehicle is “at” the intersection, and is currently in the “Stop” state and 3 seconds have elapsed. Suppose the only dynamic obstacle is “on” the intersection has a heading of 180 degrees relative to the ego heading, and suppose the ego vehicle intends to turn left. Which state will the state machine transition to?

1 point

Decelerate to Stop

Follow Leader

Stop

Track Speed

16.
Question 16
Which of the following are disadvantages of using a single state machine to handle multiple scenarios?

1 point

Complicated to create and maintain all possible cases

Rule explosion when adding new scenarios to the state machine

The amount of computation time required at each step

Not able to handle a small set of scenarios

17.
Question 17
True or false, an example of a hierarchical state machine in the behavioural planning context involves superstates representing each potential scenario and substates representing the maneuvers to be handled in each scenario.

1 point

True

False

18.
Question 18
Following the hierarchical state machine introduced in Module 5 Lesson 4, if we are exiting the intersection and we are currently in the “Intersection Scenario” superstate, which substates of the “Intersection Scenario” will allow us to change to a different superstate?

1 point

Stop

Track Speed

Follow Leader

Declerate to Stop

19.
Question 19
True or false, the hierarchical state machine is immune to the effects of rule explosion.

1 point

True

False

20.
Question 20
True or false, the hierarchical state machine limits the amount of computation time at each time step by restructuring the search space more efficiently.

1 point

True

False

21.
Question 21
Which of the following are some issues with the state machine approaches presented in Lessons 1-4?

1 point

There is no method to handle multiple scenarios when using state machines

The state machines discussed are only able to handle noise in very limited situations

The number of hyperparameters required increases as the behaviours get more complex, and inputs get more noisy

State machines are unlikely to handle situations that have not been explicitly programmed

22.
Question 22
What is an advantage of rule based systems over state machines?

1 point

Rule based systems can handle multiple scenarios

Rule based systems do not duplicate transitions, as rules can apply throughout significant portions (or all of) the ODD

Rule based systems do not require as much attention as state machines do, as rules do not impact one another

None of the above

23.
Question 23
True or false, fuzzy logic systems are more robust to environmental noise than traditional discrete systems, such as a finite state machine.

1 point

True

False

24.
Question 24
True or false, reinforcement learning involves clustering unlabeled data to inform the behavioural planner on the best course of action in each scenario.

1 point

True

False

25.
Question 25
Which of the following are some of the shortcomings of reinforcement learning approaches for behavioural planning?

1 point

It is challenging to perform rigorous safety assessment or safety guarantees of learned systems, as they are largely black boxes

Reinforcement learning is unable to handle continuous variables, such as the distance to a dynamic obstacle, and these are commonly used in behavioural planning

Reinforcement learning do not generalize well to scenarios that weren’t explicitly programmed

The model simplicity used for reinforcement learning means the results transfer poorly to real-world scenarios

## Week- 6

### Module 6 Graded Quiz

1.
Question 1
True or false, a kinematic model gives the equations of motion for our robot, while disregarding the impacts of mass and inertia.

1 point

True

False

2.
Question 2
True or false, a dynamic model is a model used exclusively for rotating robots.

1 point

True

False

3.
Question 3
For the bicycle model, the state of the robot contains which of the following values?

1 point

Y Position

Curvature

X Position

Heading

4.
Question 4
For this image, assuming each path is of equal length, what input parameter to the bicycle model is being varied across each path?

1 point

Steering Angle

Velocity

5.
Question 5
True or false, implementing trajectory propagation recursively is slower than computing the entire sum at each step.

1 point

True

False

6.
Question 6
Why is collision checking computationally challenging in exact form?

1 point

It requires perfect information about the surroundings

It requires heavy geometric computation in a continuous domain

The problem scales with the number of obstacles in a given scene

All of the above

7.
Question 7
What is the swath of an autonomous vehicle as it drives along a path?

1 point

The region surrounding an autonomous vehicle that is occupied by static obstacles in a given driving situation

The union of all sets of space occupied by the autonomous vehicle as it traverses the path

The entire region surrounding an autonomous vehicle that is safe for traversal in a given driving situation

8.
Question 8
Suppose the ego vehicle is currently at the origin, (0.0, 0.0, 0.0), and one of the points in its footprint is at (0.5, 0.5). One point along the ego vehicle’s path is (3.0, 2.0, pi/4).
After performing rotation and translation on this footprint point relative to this path point, what is the footprint point’s corresponding position?

1 point

(3.0, 3.0)

(3.0, 2.707)

(3.707, 3.0)

(3.707, 2.0)

9.
Question 9
True or false, swath-based collision checking sweeps the ego vehicle’s footprint along its path, and checks to see if any obstacles lie within this set of space.

1 point

True

False

10.
Question 10
Which of the following is not true about circle based collision checking?

1 point

It uses circles to quickly estimate collision points by checking if the distance to an obstacle is less than any circle radius

It uses the friction circle to estimate how close the ego vehicle can be to nearby obstacles

It relies on discretizing the path into a sequence of points that the circles can be rotated and translated to

It conservatively approximates the vehicle footprint using multiple circles

11.
Question 11
To generate a set of arcs in the trajectory rollout algorithm, which input needs to be varied in our bicycle model?

1 point

Steering Angle

Velocity

Angular Acceleration

Heading

12.
Question 12
What is the objective function used in the trajectory rollout algorithm for determining which trajectory to select from the trajectory set?

1 point

Minimize the integral of heading changes along the path

Minimize the distance from end of trajectory to goal

Minimize the total absolute jerk along the path

Maximize the distance from obstacles along the path

13.
Question 13
True or false, for a fixed velocity, larger steering angles will result in larger curvatures in our bicycle model.

1 point

True

False

14.
Question 14
True or false, the trajectory rollout algorithm finds an optimal path to the goal state according to the kinematic model.

1 point

True

False

15.
Question 15
True or false, the trajectory rollout planner is always able to find a path to the goal state, if one exists.

1 point

True

False

16.
Question 16
True or false, linear velocity is a higher-order term in the kinematic bicycle model.

1 point

True

False

17.
Question 17
What is the purpose of dynamic windowing?

1 point

To allow the trajectory rollout algorithm to see farther ahead into the planning process.

To improve the maneuverability of the vehicle when performing trajectory rollout.

To ensure the angular acceleration and linear acceleration lie below a set threshold

18.
Question 18
Suppose we have a bicycle model travelling at constant velocity v = 1.0 m/s, and length L = 1.0 m. If the time between planning cycles is 0.1 seconds, the previous steering angle delta_ 1 was 0.0 rad, and the current steering angle is 0.5 rad, what is the approximate angular acceleration?

1 point
Enter answer here

 5.46

19.
Question 19
Suppose we have a bicycle model travelling at constant velocity v = 1.0 m/s, and length L = 1.0 m. If the time between planning cycles is 0.1 seconds, and the previous steering angle delta_ 1 was 0.0 rad. If the maximum angular acceleration is 2.5 rad/s^2, can a path with delta_2 = 0.2 rad be selected this iteration?
A. Yes
B. No

1 point

Yes

No

20.
Question 20
Suppose we have a bicycle model travelling at constant steering angle delta = 0.0 rad, and length L = 1.0 m. If the time between planning cycles is 0.1 seconds, the previous velocity was 20.0 m/s, and the current velocity is 20.5 m/s, what is the approximate linear acceleration?

1 point
Enter answer here

 5

Download

Download