True or False PowerPoints Flashcards
(141 cards)
1
Q
Traffic safety is one of the largest health problems worldwide.
A
True
2
Q
The Heinrich’s triangle may help understand the relation among crashes with different severity level.
A
True
3
Q
Crash databases have little information about driver behavior.
A
True
4
Q
The driver behavior is a contributing factor in 80-90% of the crashes.
A
True
5
Q
Driver impairment includes physical disabilities.
A
False
6
Q
The pre-crash phase is typically longer than the in-crash phase.
A
True
7
Q
An active safety system must act before a crash happens.
A
True
8
Q
Passive safety aims at preventing crashes.
A
False
9
Q
The headrest is a passive safety system.
A
True
10
Q
By increasing exposure, injuries can be reduced.
A
False
11
Q
Active safety systems use sensors to understand the world, algorithms to assess threats and decide interventions, and HMI/actuators to issue warnings or initiate automated interventions.
A
True
12
Q
Active safety systems issue warnings via an HMI.
A
True
13
Q
ACC is a lateral control system providing sustained support.
A
False
14
Q
The only sensor needed for ACC is a radar.
A
False
15
Q
The ACC sensors are typically sufficient to also generate collision warnings andprovideheadwayinformationtothedriver.
A
True
16
Q
The Haddon matrix provides a framework for safety analysis.
A
True
17
Q
Intelligent transport systems not only are concerned about safety, but also
about environment- and mobility-related issues
A
True
18
Q
ADAS are installed inside a vehicle, ITS not necessarily.
A
True
19
Q
If a system is able to recognize a safety critical situation, then it is an active safety system.
A
False
20
Q
Active safety systems should always issue a warning in the pre-crash phase.
A
False
21
Q
Lane departure warning is an intervention system
A
False
22
Q
Understanding the driver may help active safety systems to identify critical
situations that may develop in crashes.
A
True
23
Q
Signals are transferred in digital format across ECUs.
A
True
24
Q
In general, by digitizing a signal, we loose information.
A
True
25
Sample frequency is an indicator of how much information
| we “lost” in term of time-resolution.
True
26
The number of bits of a digital signals inform us about its resolution and the quantization error.
True
27
If we convert an analog signal to digital and use 10 bits, the digital signal can only assume 1024 values.
True
28
Use an extremely high sampling frequency and large bit
| resolution is the best solution to code digital signals.
False
29
ECU may communicate in different ways, a common one T being via the CAN bus.
True
30
The (CAN) bus reduces the wiring needed to connect all sensors and ECUs.
True
31
Analog signals are transferred on the CAN bus.
False
32
The CAN bus is very robust.
True
33
It is possible to log and access the CAN bus in real time with
a PC.
True
34
Each frame have a different priority which is used to avoid frame collisions on the CAN bus.
True
35
The DBC files explain the priority of the different ECUs.
False
36
Not all signals can be represented as a distribution
False
37
In a normal distribution mean and median are the same.
True
38
RMS and standard deviation have the same value for infinite signals with mean equal to zero
True
39
By representing a signal as a distribution we can visualize the probability for the signal to assume different values.
True
40
In a normal distribution most of the samples are within one
| standard deviation from the mean.
True
41
In a normal distribution the probability for a sample to be
| within one standard deviation is >76.32 %.
False
42
Digital filtering is implemented using mathematical functions.
True
43
A moving average filter is more robust than a median filter and cannot
provide impossible values.
False
44
A high-pass filter stops high-frequency noise
False
45
An exponentially weighted moving average filter is a first-order low-pass filter.
True
46
A first-order low pass filter introduces a delay and this delay is larger the higher the frequency is.
True
47
The cut-off frequency of a low-pass filter gives an indication of which frequencies we may expect to pass or not pass through that filter.
True
48
If a frequency f is attenuated by 60 dB by a filter it means that on the output
we will only see only one millionth of the amplitude of f.
True
49
A Kalman filter is an algorithm for optimal state estimation for linear systems in which controls and measures are affected by gaussian noise.
True
50
It is sufficient one single measure (either a control or an output to implement Kalman filtering).
False
51
Kalman filtering is used for sensor fusion in active safety systems.
True
52
Kalman filters work in two steps, prediction and correction.
True
53
Kalman filters make the best out of the known information by determining which information we should trust the most (at each loop) to estimate our state.
True
54
Unscented KF and extended KF provide an alternative to Kalman filter when the error is not gaussian.
False
55
Particle filters are very general, they do not require for a systems to be linear nor for the error to be gaussian distributed, but they are very computationally demanding.
True
56
Crashes happen when drivers exceed their comfort zone.
False
57
Drivers do not optimally control their vehicles.
True
58
Crash scenarios need to be determined and a causation mechanism understood before active safety systems may be developed.
True
59
A crash may happen as a consequence of an adaptation failure such as the driver underestimating the deceleration of the vehicle in front.
True
60
A typical crash scenario for lane departure warning would be a run-off-road crash
True
61
A typical use case for a distance alert system would be based on a car-following situation.
True
62
Over time, active safety systems improve by increasing the number and variety of crash scenarios and use cases that they can address.
True
63
TTC is lower than TH if the vehicle in front is braking.
True
64
TTC = TH for stationary objects.
True
65
Required deceleration may be very different even when TTC is the same.
True
66
Threat assessment algorithms may estimate in real-time the necessary required jerk and required steering rate to avoid collision.
True
67
Decision making algorithms base their decision of what a driver and the vehicle are able to do (driver models).
True
68
For rear-end scenarios, if speed is above 60 km/h and TTC below 0.5s, AEB may be the only way to help, but we will still crash
True
69
A heartbeat in a cooperative system is a periodic signal.
True
70
It is possible to implement a cooperative system without using wireless communication.
False
71
Platooning (cooperative ACC) requires that at least 80% of the cars in the platoon are equipped.
False
72
A critical mass of at least 10% equipped car is necessary for most of the
cooperative applications to be effective.
True
73
In a cooperative application the ego-vehicle is the vehicle getting the warning.
False
74
Floating car data is data collected by loggers inside cars in specific safety- relevant situations.
False
75
Cooperative systems always require broadcast communication
False
76
Longer latencies are desirable for the development of cooperative safety applications
False
77
Only limited amount of information should be transmitted at high frequency rate in order ro limit the use of the bandwidth
True
78
The “beacon” should contain little information since it is a periodic signal and many vehicles may be transmitting it simultaneously.
True
79
An LDM is a common database which each vehicle can assess wirelessly
False
80
It is necessary to have strict regulations about transmission power only to limit energy consumption.
False
81
Beaconing/BSM alone can enable specific cooperative applications.
True
82
Naturalistic data is field data, however, field data is not necessarily collected in a naturalistic fashion.
True
83
Naturalistic datasets normally do not include subjective data.
False
84
Field data has a very high quality, especially in naturalistic studies.
False
85
Naturalistic data is not biased (or affected by confounding variables).
False
86
Typically, naturalistic dataset includes subjective and objective data; further, video and data from extra sensors (e.g. radar or eye tracking) are very often
available in naturalistic datasets.
True
87
Data enrichment never requires access to additional datasets.
False
88
Naturalistic data contains information about the pre-crash phase which is
not available in accident databases.
True
89
Naturalistic data contains information about driver behaviour which is not available in accident databases
True
90
By selecting data for our analysis we may bias our results.
True
91
Keeping track of the data quality is a prerequisite for any field data analysis.
True
92
Time to collision is often a derived measure (added later on in the database).
True
93
Distraction and secondary tasks are manually coded.
True
94
Statistics are means to demonstrate how performance
indicators may be different between the treatment and
baseline phase of a FOT.
True
95
An FOT collecting data in a naturalistic fashion does not
| have an experimental protocol to control for driving scenarios nor hypotheses or research questions.
False
96
Automated driving promises to make our roads safer by reducing human error.
True
97
ACC can reach a level 1 of automation
True
98
ACC+LKA can reach a level 3 of automation.
False
99
Automation level 3 and 4 can be achieved at the same time
False
100
In automation level 5, the machine is responsible for fallback and achieving a minimal risk condition.
True
101
Two possible strategies for autonomous vehicles take off are envisioned: ”everything anywhere”, and ”something somewhere”.
False
102
The are many open issues to be solved before autonomous driving takes off. These issues are not only technical but, for instance, regard human factors.
True
103
Driver models can be built using different types of data (e.g. Naturalistic Driving Data, simulator driving data)
True
104
Driver models cannot be used to support the design of automated driving
False
105
The PP account of cognition assumes that actions are taken when there is a mismatch (error) between actual and predicted sensory signals
True
106
In conflicts where lead vehicle is braking, the distribution of maximum drivers’ deceleration is an example of driver model
True
107
Driver models can be used for the prediction of the safety benefits of active safety systems
True
108
In compensatory control, driver applies control on the vehicle in order to minimize the error between desired and actual value of a specific variable
True
109
Control models provide a quantitative assessment of driving behaviour
True
110
Control models take into account drivers’ motivation and goals and their effect on the driving task
False
111
Based on the zero-risk theory, motivation drives the expectancy of how a driving situation will evolve
True
112
According to the zero-risk theory, drivers avoid behaviour that elicits fear
True
113
Driver’s reaction in rear-end conflicts depends on the kinematic criticality of the driving situation
True
114
According to the risk homeostasis theory, the introduction of a safety system might have negative effects on road safety
True
115
In the Hierarchical Control Model, the strategic level is responsible for controlling the braking and acceleration during normal driving
False
116
In the Hierarchical Control Model, the driving sub-tasks at upper level have an influence on the driving sub-tasks at lower level
True
117
Auto Emergency Braking (AEB) would support the driving task at strategic level in Hierarchical Control Model
False
118
Hierarchical models provide mainly quantitative results
False
119
Subjective risk is the same for all drivers
False
120
Drivers seek no risk and avoid behaviour that elicits fear or anticipation of fear
True
121
Driver’s motivation influences the expectancy of how a driving situation will evolve
True
122
Driver’s motivation does not influence the perception of stimuli from the external environment
False
123
The Deming’s cycle explains how system development and evaluation may happen in a continuous loop to improve the system.
True
124
A validation plan is often more comprehensive than a test plan and may include human factor issues.
True
125
A cost-benefit analysis may be able to tell how much money society may save as the percentage of vehicles with AEB increases.
True
126
A TRL9 system is probably on the market, a TRL2 system may not even be T a prototype.
True
127
Active safety develops by increasing the ODD and OEDR of the systems.
True
128
As a consequence, the validation plan of a system addresses increasingly more complex and various scenarios as the system develops.
True
129
One of the main advantages of driving simulations is the possibility to explore safety critical situation with good repeatability.
True
130
Test tracks can expose a vehicle to different environments such as slippery road.
True
131
The ecological validity of test-tracks and driving simulators is hard to measure and still to be proven.
True
132
Weather test-tracks or driving simulators are the best environment for active safety development depends on the specific research questions of the study.
True
133
Field operational test are performed as soon as a prototype is available.
False
134
Increasingly complex and costly test methodologies follow an active safety system development.
True
135
Computer simulations are increasingly supporting the development and evaluation of active safety systems.
True
136
Cooperative simulations can access the extent to which cooperative systems may impact traffic at different penetration rates.
True
137
Counterfactual simulations can estimate the safety benefit of a new active safety system even at low TRL/MRL.
True
138
Driver models are necessary for a counterfactual simulation if the purpose is to compare different warning strategies.
True
139
Counterfactual simulations may help determine if a new software upgrade for active safety (or AD) is safe.
True
140
If a warning system has specificity 1 cannot have sensitivity 1
False
141
If the specificity of a system is 0.5 and the sensitivity is 0.5, we can just as well flip a coin.
True
