EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie

Karteikarten und Zusammenfassungen für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie

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Lerne jetzt mit Karteikarten und Zusammenfassungen für den Kurs EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie.

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

How can test scenarios be created?

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

Name the two different testing systems and their  advantages and disadvantages

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

What is the difference between Verification and Validation?

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

Name and explain some Optimizing Planners

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

How's the Dijkstra Algorithm working?

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

Explain Bellman's Principle of Optimality

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

Explain Dynamic Optimization in the context of ADAS

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

How can a neural network be trained?

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

How could a traffic sign recognition be implemented?

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

What is a random forest and its benefits?

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

How can model quality be measured?

Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

Explain principle of supervised learning

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Beispielhafte Karteikarten für EM3: Driver Assistance Systems an der Karlsruher Institut für Technologie auf StudySmarter:

EM3: Driver Assistance Systems

How can test scenarios be created?

Case by case vs. Traffic based

Data-driven vs. Knowledge driven

EM3: Driver Assistance Systems

Name the two different testing systems and their  advantages and disadvantages

Open loop testing

– Sensor measurement, no impact of subject under test on environment

+ Easy data acquisition

+ Replay of sensor data

– Restricted to perception and environment algorithm

– no feedback

Closed loop testing

+ Integrative testing

+ Feedback of environment is considered

– Validity of model based behaviour

EM3: Driver Assistance Systems

What is the difference between Verification and Validation?

The distinction between the two terms is largely to do with the role of specifications. Validation is the process of checking whether the specification captures the customer’s needs, while verification is the process of checking that the software meets the specification.

Validation: Are we building the right system?

–> Rapid Prototyping 

–> Acceptance Test 

Verification: Are we building the system right?

–> Review: Walktrough, Code Inspection 

–> Analysis: Formal Proof 

–> Dynmaic methods: black/white box testing 

EM3: Driver Assistance Systems

Name and explain some Optimizing Planners

In some motion planning problems, you might not want just any valid path between your start and goal states. You might be interested in the shortest path, or perhaps the path that steers the farthest away from obstacles. In these cases you’re looking for an optimal path: a path which satisfies your constraints (connects start and goal states without collisions) and also optimizes some path quality metric. Path length and path clearance are two examples of path quality metrics. Motion planners which attempt to optimize path quality metrics are known as optimizing planners.

Sequential Quadratic Programming (SQP)

– Iterative nonlinear optimization of constrained optimization problems

– Objective function and constraints must be continuously differentiable twice to compute the Jacobian and Hesse Matrices

Particle Swarm Optimization (PSO)

– Inspired movement of organisms in a bird flock or fish schools

– Nonlinear optimization of constrained optimization problem

– Anytime algorithm (can be aborted at any time and still yield a valid (suboptimal) result)

– No gradient of cost function needs to be calculated (helpful in areas close to constraints as no gradient can be determined there)

– Sampling of possible trajectories (a particle is a trajectory!)

EM3: Driver Assistance Systems

How's the Dijkstra Algorithm working?

􏰀 Search for the shortest path from a start node to a goal node along a given graph

􏰀 All nodes of the graph inserted in a queue to calculated the minimal accumulated costs from the start node to a goal node by adding up the transition costs.

EM3: Driver Assistance Systems

Explain Bellman's Principle of Optimality

An optimal policy has the property that whatever the initial state and initial decision are, the remaining decisions must constitute an optimal policy with regard to the state resulting from the first decision

The overall trajectory is optimal if and only if every residual trajectory is optimal for every intermediate state (e.g. Parking assistant)

EM3: Driver Assistance Systems

Explain Dynamic Optimization in the context of ADAS

In the context of ADAS, problems can be described as optimal control problems (State and Control time variant )

– The system dynamics describes the vehicle‘s dynamics (e.g. longitudinal behavior)

– The optimization variables describe the vehicle‘s trajectory.

– The cost function and objectives describe the desired behavior (i.e. safety,

comfort and efficiency).

– The constraints guarantee collision free and feasible trajectories.

EM3: Driver Assistance Systems

How can a neural network be trained?

Task is to find optimal weight matrix. Based on gradient descent rule to minimise error function. Called back propagation since error is propagated from the output through the network. 

Learning can be done on each pattern, a subset (state of the art) or using the whole epoch. 

Gradient descent can be optimised by using momentum term (change of gradient) in formula for step size. Adam Adaptive moment estimation state of the art. 

Problem of overfitting and early stopping as solution also applies to neural networks.

Amount of data as biggest problem. 

EM3: Driver Assistance Systems

How could a traffic sign recognition be implemented?

Combination of multiple algorithms.

1. Segmentation (find border of objects)

2. Sort signs into different classes with decision tree

3. Concrete classification based on decision tree node with support vector machine.

EM3: Driver Assistance Systems

What is a random forest and its benefits?

Multiple decision trees that are calculated and end result is calculated based on sum of individual results.

Devide data set so that each tree acts on subset of attributes.

+ Fast parallelizeable training

+ Smaller individual trees

+ Efficient for large data amounts

EM3: Driver Assistance Systems

How can model quality be measured?

Metrics to describe how accurate a system performs for a given task are required.

Error types:

– True positive

– True negative

– False Positive

– False Negative

Different metrics available – one alone is not sufficient

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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Control system development

Electronic System Synthesis

Embedded Systems Computer Architecture

MM2: Management Accounting

Finance and Value

MM2: Financial Accounting

Finance and value

EM3: Car-to-X Communication

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