The document presents an adaptive cognitive system designed to enhance the driving tasks of future vehicles through a co-driver model that understands human driver intentions and actions. It emphasizes the importance of driver cooperation and the need for the co-driver to emulate human-like sensory-motor behaviors to ensure efficient vehicle control. Key technologies discussed include simulation theories of cognition, generative approaches for agent behavior, and a hierarchical behavioral model inspired by human motor control.

1. What has been done what need to
be done.
An Adaptive Cognitive System to Support
the Driving Task of Vehicles of the Future
Francesco Biral
University of Trento
francesco.biral@unitn.it

2. What do we need to do?7
What I will talk about today?
What kind of technology do we need to support drivers and
interact with them in complex road scenarios?

3. Self driving cars does not take driver into the loop8
Self driving cars are only a part of the answer
Will be an artificial robot that drivers as an expert human driver
sufficient?

4. Will the driver accept robotic cars?
9
Potential reactions of some customers
certain segments of the population will be less likely to
embrace autonomous driving (e.g. Car enthusiasts)
The “Digital Natives” and “Gen. Now” generations’ identity
is less likely to be attached to the “driving experience.”

5. like horse and rider
like a driving instructor
Driver and machine need to cooperate
10

6. We must understand/know the driver
We must know his/her goals and intentions and driving abilities11
A human would drive in distinctly different ways
depending on whether his/her goal is.
depending on his/her driving skills and experience

7. Like in the riding-horse metaphor
understand your intentions
understand your driving abilities
silently and gently support you when necessary
improving your manoeuvre
executing autonomously a manoeuvre initiated
execute a task when required (eg. take me there)
leave you the control but intervene only when driver reaches his/her
limits or underestimate a scenario or did not see a better option
The ideal support
12

8. Design principles of Co-Driver
Definition of Codriver
Architecture
The simulation theory of cognition and human like sensory motor
cycles
Co-Driver developed in EU interactIVe project
Instantiation of a CoDriver
Experimental examples
Limitations
Impact of Co-Driver technology & research lines
Contents
13

9. What a Co-Drivers is? Theoretical background

10. Incomplete definitions
Autopilot (emphasis on automatism)
Companion Driver (robot: emphasis on human-robot interaction)
Virtual User (alter-ego: emphasis on reproducing human skills)
Driver Tutor (emphasis on supervising)
Natural co-drivers exist (animals and especially horses – H-
metaphor, Flemish, Norman, et. al.)
What a Co-Driver might be?
What is a codriver?15

11. A ”co-driver” is an intelligent agent which:
Understands human driver intentions.
Produces human-like motor behaviours.
has an internal structure that copies the human one
(at least in some sense)
Interacts accordingly and rectifying mistakenly executed actions.
Characteristics of a Co-Driver
What a CoDriver is?16

12. Co-Driver must “understand” human driver
How would a human drive?
This question has multiple answers!
Answer depend on some higher level motivations/goals.
The co-driver must put himself “in the shoes” of the human
driver and understand the goal.
It is not an easy task since there are a multiplicity of goals
Key Technology #1
17

13. Co-driver must understand the goals of the human driver
(humans can do that with other humans, how do they do?)
Simulation theory of Cognition is a conceptual framework which
essentially states that “thinking is simulation of perception and
action”, carried out as covert motor-sensory activity.
Also understanding of others’ intentions is also a simulation
process, carried out via the “mirroring” of observed motor
activities of others.
Hurley, S.L., 2008. The shared circuits model (SCM): how control, mirroring, and simulation can
enable imitation, deliberation, and mindreading. Behav. Brain Sci. 31, 1–58.
Grush, R. 2004. "The Emulation Theory of Representation: Motor Control, Imagery, and
Perception." Behavioral and Brain Sciences 27 (3): 377-396.
Jeannerod, M. 2001. "Neural Simulation of Action: A Unifying Mechanism for Motor Cognition."
NeuroImage 14 (1 II): S103-S109.
Understand the goal: theoretical background
18

14. Generative Approach is to generate agent behaviours under a
number of alternative hypotheses, which are then tested by
comparison with observed behaviours.
“multiple simulations” are run in parallel, and the most salient
one(s) are selected).
The observed behaviour identifies the internal states of the
observed agent, and thus the intentions
(Haruno, Wolpert, and Kawato 2001; Demiris and Khadhouri 2006).
“Like me” framework for understanding of others’ intentions:
“others who act like me have internal states like me” (Meltzoff).
The “like-me” framework essentially states that one agent “stands
in the shoes of another”.
Simulation/mirroring theories of cognition
19

15. Summing up: agents with similar sensory-motor system
and capable of covert motor activities can use their
sensory-motor system to “simulate” observed actions, and
thus know the intentions of the observed agent
“putting the co-driver in the shoes of the real driver” means
the co-driver “emulates” the real driver such as in covert
motor activities.
Objective: link driver behaviour to meaningful goals
(understand driver goals/motivations).
Simulation/mirroring theories of cognition
20

16. Exemplification of terms used
21
longitudinal control
lateral
control
1 2 3 4 5 6 7 8
3c
3a
3b
Goals
Driver
behaviours
Map of controls
alternative
hypotesis
covert motor
activities

17. Co-driver must be able to generate human like motor primitives:
Humanlike. Reproduce human sensory-motor strategies (path
planning and motor patterns just like a human).
D Liu, E. Todorov, Evidence for the Flexible Sensorimotor Strategies
Predicted by Optimal Feedback Control, Journal of Neuroscience, 2007 •
27(35):9354 –9368
P. Viviani, T. Flash, Minimum-jerk, two-thirds power law, and isochrony:
converging approaches to movement planning. J. Exp. Psychol. 21: 32-53,
1995.
“Even if skilled performance on a certain task is not exactly optimal,
but is just ‘good enough’, it has been made good enough by
processes whose limit is optimality”.
Human motor patterns respond to optimality criteria and may be
reproduced by Receding Horizon Optimal Control (minimum
intervention principle)
Key Technology #2
22

18. Experimental data
drivers reduce speed in curves to maintain the accuracy in lateral position
23
Acceleration patterns and Two third law
In order to improve movement accuracy, while preserving average speed, it is
convenient to increase speed in straighter arcs and reduce it along curvier ones.
alat =
a0
s✓
1
⇣
v
v0
⌘2
◆2
+ 2
⇣
v
v0
⌘2
v =
↵
3
p


19. Architecture#1: sense-think-act paradigm
It is the traditional architecture of AI also known as computer metaphor.
The central idea is the existence of an “internal model of the world”.
Problems:
perception “per se”;
not scalable (interfaces are choke points);
difficult to test;
is not what happens in the human brain;
not fault tolerant;
hard to conceal with motor imagery and covert sensory-motor activity.
Key Technology #3: Architecture
24

20. A tutor made of a one-level virtual driver (called “reference
maneuver”) was built into SASPENCE and INSAFES (+ evasive
maneuver).
Limitation: missing motor imagery it was not able to
“understand” the driver goal (giving recommendation for a
pre-defined goal).
Da Lio, Biral et. al, T-ITS, 2010 (2 papers)
Sense-think-act success story/1
25

21. (Versailles test track: reference manovre (red) vs. real driver
(blue) movie)
Sense-think-act success story/2
26

22. Optimal control replanning on 200m horizon every 0.1s
Behaviour on a curve
Other examples
27

23. Decomposition in parallel behaviours (hierarchical levels of
competence).
Is based on Perception-Action cycles (no internal model of the
world).
Multi-goal, multi-sensor (perceptual synthesis), robust, scalable,
subsumptive, each level includes sub-level competences.
R. A. Brooks. A robust layered control system for a mobile robot. IEEE
Journal of Robotics and Automation, 14(23), April 1986.
Architecture: the behavioural model
28

24. Shared Circuit Model combines the above ideas into an
interpretative scheme named with in the behavioural
architecture
“Thinking” is a simulated interaction.
Emulation theory of cognition (Grush, Hurley, Jannerod, et al.)
enables imitation, motor imagery, deliberation, mindreading,
understanding….
Theory of Cognition by means of emulation
29
PERCEPTION ACTION
INVERSE MODEL
FORWARD EMULATOR
ENVIRONMENT
IMITATION SIMULATED
INPUT
OWNS
ACT
BODY
OTHER'S
ACT
OUTPUT
INHIBITED

25. The general idea that the brain monitors a small number of task
parameters y instead of the full state x, generates abstract commands
v, and maps them into muscle activations u using motor synergies.
Humans are organized in hierarchies of subsumptive behaviors
(Brooks, 1986; Michon 1985; Hatakka et al. 2002; Hollnagel and Woods 1999,
2005).
Human cognition is “grounded” in which the intelligent agent is seen in
the loop with the environment, and perception and action are no longer
divided.
(Gibson 1986; Varela, Thompson, and Rosch 1991; Thelen and Smith 1994; Van
Gelder 1995; Harvey 1996; Clark 1997; Seitz 2000; Beer 2000; Barsalou 2008)
The traditional paradigm of AI (the computer metaphor: input-
processing-output) suffers symbol grounding problems of the abstract
amodal symbol systems. It fails in modeling mutual understanding of
agents.
Human-like sensory-motor systems
30

26. The ECOM is a subsumptive hierarchical behavioural model
of human driving.
(Hollnagel and Woods, 1999, 2002, 2005)
Successfully used in FP7 DIPLECS.
The Extended Control Model (ECOM)
31
Long term goals and psychological states
(e.g., go home quickly)
Short term goals and driving styles.
(e.g. overtake “a” instead of “b”).
Space-Time Trajectories
(i.e., including speed).
Vehicle control.

27. It is inspired by this general organization of the sensorimotor system.
The low-level controller receives information about the plant state x, and
generates an abstract and more compact state representation y(x) that is
sent to the high level. The high-level controller monitors task progress,
and issues commands v(y) which in general specify how y should change.
The job of the low-level controller is to compute energy-efficient controls
u(v,x) consistent with v. Thus the low-level controller does not solve a
specific subtask (as usually assumed in hierarchical reinforcement
learning), but instead performs an instantaneous feedback transformation.
This enables the high level to control y unencumbered by the full details
of the plant.
Hierarchical control scheme
32
act with the musculoskeletal system directly: They re-
ceive rich sensory input, and generate corresponding
motor output before the rest of the brain has had time
to react to that input. Higher-level circuits interact
with an augmented plant, that consists of the lower
levels and the musculoskeletal system. The lower lev-
els perform a ͑not well understood͒ transformation,
allowing higher levels to operate on increasingly
more abstract and more goal-related movement
representations.4
Here, we propose a hierarchical control scheme
inspired by this general organization of the
sensorimotor system, as well as by prior work on hi-
erarchical control in robotics.5–7
We focus on two-
level feedback control hierarchies as illustrated in
Figure 1. The low-level controller receives informa-
tion about the plant state x, and generates an abstract
and more compact state representation y͑x͒ that is
sent to the high level. The high-level controller moni-
tors task progress, and issues commands v͑y͒ which
in general specify how y should change. The job of
the low-level controller is to compute energy-efficient
controls u͑v,x͒ consistent with v. Thus the low-level
controller does not solve a specific subtask ͑as usually
assumed in hierarchical reinforcement learning͒,8,9
but instead performs an instantaneous feedback
another motivation for the present scheme
blown optimization on redundant tasks is k
yield hierarchical structure, it makes sense to
the optimization to an ͑appropriately chosen
of hierarchical controllers.
The general idea that the brain monitors
number of task parameters y instead of the
x, generates abstract commands v, and ma
into muscle activations u using motor syner
been around for a long time.13,14
Anumber of
models of end-effector control have been for
in the context of reaching tasks.15–20
The h
state in such models is assumed to be hand
the abstract command is desired velocity
space or in joint space, and the high-level c
is a simple positional servo. While these mo
related to our work, in some sense they leav
hard questions unanswered: It is unclear how
parameters are actually controlled ͑i.e., what
responding muscle synergies are͒, and whe
choice of task parameters can yield satisfact
formance. We address these questions here.
Our framework is related in interesting
input-output feedback linearization21,22
as w
the operational space formulation6
—which a
the general scheme in Figure 1. These metho
linear dynamics on the high level, by cance
plant nonlinearities at the low level. Howev
systems of interest cannot be linearized, and
more it is not clear that linearization is des
the first place. Suppressing the natural plant
ics may require large control signals—which
ergetically expensive, and also increase erro
tems subject to control-multiplicative n
universal characteristic of biological movem
In contrast, we summarize the plant dynami
high level and thus create opportunities for
ing them. Recent work in biped locomotion2
scores the potential of such approaches. In
our objective is dimensionality reduction rat
Figure 1. Schematic illustration of the proposed
framework.
Journal of Robotic Systems 22(11), 691–710 (2005)

28. CoDriver in InteractIVe CRF Demonstrator

29. Design an agent capable of enacting the “like-me” framework,
which means that it must have sensory- motor strategies similar
to that of a human, and that it must be capable of using them
to mirror human behaviour for inference of intentions and
human-machine interaction for preventive safety, emergency-
handling and efficient vehicle control.
Implemented
Four layers ECOM-like behavioural subsumptive architecture.
Forward/mirroring mechanisms (by Optimal Control).
Motor imagery, inference of driver’s goals.
Main goal
34
states goals

30. Forward emulators are vehicle dynamics models that neglect high
frequencies (not afforded by humans) but consider non-linarites.
A predictive model here serves to test the viability of different hypotheses of
human driving intentions. Thus, its main requirement is similarity to humans’
used model (if not, co-driver predictions will not match observations even for
a correct hypothesis).
we make the assumption that slow, if non-linear, phenomena are capable of
being human-directed whereas faster ones are not (due to human actuation
limits and band width).
Inverse emulators are minimum jerk/minimum time optimal control
(OC) plans that links perceptual goals (i.e. desired states) to the
actions needed to achieve those goals
Other approaches may be based on machine learning; for example, the
learning of either or both inverse and forward models.
OC has the advantage that needs knowing only the forward model and
optimality criteria
Building blocks#1 - Emulators
35

31. Motor primitives are parametric instantiations of inverse
emulators that achieve specified goals. They are the solution
of inverse models that determine the (optimal) control
required to reach a desired state a some future time T.
Since there may be several types of final states and optimization
criteria, the inversion problem produces a corresponding number
of solutions, which we may regard as different motor primitives
parameterized.
There are 4 motor primitives:
Speed Adaptation (SA)
Speed Matching (SM)
Lateral Displacement (LD)
Lane Alignment (LA).
Building blocks#2 – Motor primitives
36

32. Motor primitives
37
Example for longitudinal dynamics #1
Optimal control formulation with some simplifications
d
dt
s(t) = u(t)
d
dt
u(t) =
1
Me
fx(p(t), u(t)) k0 kvu(t)2
| {z }
a(t)
d
dt
a(t) = kpjx(t)
J =
Z T
0
jx(t)2
+ wT dtgoal function
system model
B(x(0), x(T)) = 0Boundary conditions
J =
Z T
0
[jx(t)2
+ wT + 1(t)
✓
d
dt
s(t) u(t)
◆
+ 2(t)
✓
d
dt
u(t) a(t)
◆
+ 3(t)
✓
d
dt
a(t) kpjx(t)
◆
]dt
x = [s(t), u(t), a(t)]states

33. Speed matching
38
Example for longitudinal dynamics #2
We define the motor primitive boundary conditions
After first variation and applying Pontryagin Principle we get:
perceptual goalsBoundary conditions
r Speed Matching (SM)
B(x(0), x(T)) =
2
6
6
6
6
6
6
4
s(0) = 0
u(0) = ui
a(0) = uf
s(T) = sf
u(T) = uf
a(T) = 0
3
7
7
7
7
7
7
5
jx(t) = kp
3(t)
2
Optimal control law
d
dt
1(t) = 0
d
dt
2(t) + 1(t) = wT
d
dt
3(t) + 2(t) = 0
Co-State equations

34. Speed matching
39
Example for longitudinal dynamics #2
Given the space with can solve for the minimum time T
Non linear equation in T to be solve numerically:
a (⇣) =
✓
3
⇣2
T2 4
⇣
T
+ 1
◆
ai +
✓
6
⇣2
T3 + 6
⇣
T2
◆
uf +
✓
6
⇣2
T3 6
⇣
T2
◆
ui +
✓
⇣3
12
⇣2
8
T +
T2
⇣
24
◆
wT
sf = s (⇣) =
✓
2⇣3
3T
+
⇣2
2
+
1⇣4
4T2
◆
ai +
✓
1⇣4
2T3 +
⇣3
T2
◆
uf +
✓
1⇣4
2T3 + ⇣
⇣3
T2
◆
ui
+
✓
1
240
⇣5 1
96
⇣4
T +
1
144
⇣3
T2
◆
wT
u (⇣) =
✓
2
⇣2
T
+ ⇣ +
⇣3
T2
◆
ai +
✓
2
⇣3
T3 + 3
⇣2
T2
◆
uf +
✓
2
⇣3
T3 + 1 3
⇣2
T2
◆
ui +
✓
⇣4
48
⇣3
T
24
+
⇣2
T2
48
◆
wT
3 (⇣) =
✓
12
⇣
T2 + 8 T 1
◆
ai +
✓
24
⇣
T3 12 T 2
◆
uf +
✓
24
⇣
T3 + 12 T 2
◆
ui +
✓
⇣2
2
+
T⇣
2
T2
12
◆
wT
jx(⇣) =
3(⇣)
2
Optimal control

35. Speed matching
40
Example for longitudinal dynamics #2
for different wT: please note the different value of initial jerk

36. Space-time trajectories deals with either longitudinal or lateral control to
manage one single motor task.
There are 6 functions:
FollowObject (FO):
approach a preceding obstacle with desired time gap TH (Time Headway)
ClearObject (CO):
The purpose of this maneuver is to clear a frontal object on either side of the host
vehicle
FreeFlow (FF)
This maneuver produces a SA primitive by guessing a target speed uT
LaneFollow (LF)
LandMarks (LM)
Curves (CU)
Building blocks#3 - Trajectories
41

37. It is a combination of motor primitives
Obstacle lateral movement model: align the road in some time T*
according to the same ego-vehicle lateral forward model
first compute the encounter time T° using the longitudinal motion
models;
produce an LD primitive (i.e., parameters nT and T) such that a
specified clearance c0, is obtained at T°.
Example Clear Object
42
eview
Only
ect, th, wT )→ SM xT ,uT ,wT( ) (15)
puting the target point xT and velocity uT
llowing the object as required:
− lo
(16)
gitudinal clearance that accounts for the
icle and obstacle plus any extra desired
aimed-at time headway gap, so is the ini-
object and T is the maneuver duration,
solving (16) together with (5).
t function thus instantiates an SM primi-
s between two levels indicate this form of
ship.
of the longitudinal control:
,wT ) (17)
lar significance, because it indicates how
to drive now in order to follow the object,
ly compared with the longitudinal control
r employs.
owed object does not need to be in the host
is function to apply. If it is travelling in a
ng the case where it is behind the host car,
of intentional assessment. At this point we do not try to esti-
mate T*
, but use the heuristically-derived figure T*
~ 2.5 s (see
also next section and section IV).
The maximum lateral displacement of the object will be
achieved at t = T*
:
sn,max = sn, 0 + vn
T *
2
(19)
If this position falls within one lane of the current object
lane then model (18) is confirmed (i.e., we assume the object
is following our road, possibly changing one lane only). If not,
the object is considered to be crossing our road. In this case its
transverse motion is taken to be uniform:
sn = sn, 0 + vn t (20)
With an object predictive model (in our case the simple
Fig. 5. Evasive maneuvers.
econtounter time T°
nO = nO0 + vn
t
2
t = 0 . . . T⇤

38. Combines level 2 motor chunks into maneuvers.
Makes arrays of hypotheses, including incorrect ones that will
be used for inference of intentions.
!
Building blocks#4 – Navigation hypotheses
43
4
14
Ji = w ||j ˆj ||2
+ wp||jp
ˆjp||2
+ wnJn
wnJn = steering cost

39. Building blocks#5a – Inference of intentions
Compares co-driver motor output of the generated
hypotheses with human control (generative approach)
Use a saliency approach.
44
longitudinal control
lateral
control
1 2 3 4 5 6 7 8
3c
3a
3b
Ji = w ||j ˆj ||2
+ wp||jp
ˆjp||2
+ wnJn
wnJn = steering cost

40. Building blocks#5a – Interaction
45
Interactions are application-dependent (CRF CS is an
assistive system)
Intention is known.
If it is correct the system does nothing.
If t is incorrect, the system knows two ways to rectify it.
The system suggests the longitudinal correction, except
when the lateral correction is in lane.

41. Inference of driver intentions (model identification problem).
Implements motor imagery, imitation, mindreading (model
identification) for all meaningful goal.
Top level (goal/motivations level)
46

42. User tests Examples and discussion

43. The test route was a 53 km loop from CRF headquarters in Orbassano (Turin,
Italy) to Pinerolo, Piossasco and back, which included urban arterials, extra
urban roads, motorways, roundabouts, ramps, and intersections.
A total of 35 hours of logs have been collected, including sensor data, co-driver
output, and images from a front camera.
24 test users
Vehicle demonstrator & User Test Route
48

44. How does it work?
Experimental results49
Example #1: Car following

45. How does it work?
Experimental results50
Example #2: Pedestrian
Standing still Crossing

46. How does it work?
Experimental results51
Example #3: Cut-in manoeuvre

47. when the two agents
disagree, to assess the reason
it is necessary to manually
inspect the recordings.
Reasons for mismatch may be:
poorly-optimized co-driver
(frequent during
development)
perception noise
driver error
simple difference of
“opinions” between the two
agents (see below).
52
ForRevie
“opinions” between the two agents (see below).
Fig.8 (a) shows an example situation, which happened 1.1 s
before the event depicted in Fig.7, when, for the first time, the
co-driver detected a risk for maneuver 1.
Fig. 8. (a, top) difference in longitudinal acceleration between the two
agents; (b, center) distribution of acceleration difference; (c, bottom)
distribution of lateral position difference.
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
accepting a slightly short time
headway (0.9 s) for a while.

48. Anticipation of overtake
53
Fo
T-ITS-13-11-0605.R1
These events represent a different form of inference of in-
tentions, pertaining to a higher cognitive level, which are de-
dictive tracki
The predic
Fig. 12. Anticipation of overtake, anticipation of lane change and actual lane change for cases 2,
detected as second-level state transition (FollowObject to ClearObject behavior). Second row: la
crossing the lane). Third row: actual lane crossing.
Page 13 of 19 IEEE Intelligent Transportation Systems Transa
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2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Overtake
intention
lanechange
detected
actual
lanecrossing

49. Anticipation of overtake
54
13
in-
de-
dictive tracking of dynamic actions” [123].
The prediction of overtaking, for maneuvers 2, 6, 10, 14 and
lane change for cases 2, 6, 10, 14 and 18 (left to right). First row: overtake intention
ehavior). Second row: lane change detected as motor primitive level transition (LD
n Systems Transactions and Magazine
Overtake
intention
lanechange
detected
actual
lanecrossing
ForReview
Only
driver passes to edges during transitions (e.g., taking the first
exit ramp at cycle ~700). The numbered light green bands
stand for the lane changes. The interval between when the LD
motor primitive predicts the lane crossing and the actual cross-
ing of the lane is shaded. There are 21 changes correctly pre-
dicted, with anticipation ranging from 1.1 s to 2.4 s (median
1.6 s).
There are two false crossing predictions, labeled a, and b, that
happen in the non-motorway section when the trajectory pass-
es close to edges. Fig 10 shows the camera view to demon-
strate how demanding this situation actually is. Despite the
false prediction, the absolute value of the lateral prediction
error is limited (a fraction of the vehicle width). Non-
motorway segments are characterized by often-irregular lane
geometry, with splitting and merging lanes, often with missing
marking traits (Fig.10 b), or else the camera failing to recog-
nize them.
Fig.8 (c) shows that 0.0025 quantile curves, i.e., 99.5% of
the LD motor primitives depart from the real trajectory for less
than one quarter of lane in 2 s, less than half lane in 2.5 s and
less than one full lane in 5 s. The points where larger devia-
tions happen may be seen in Fig.9 (the prediction error is plot-
ted). They are typically at inversions of the heading angle and
in complex geometries (a and b).
In Fig.9 (b), the dashed blue vertical lines before lane
changes 2, 6 10, 14 and 18, mark the point where the co-driver
switches from FollowObject (second-level) behavior to Clear-
Object behavior. This is the point where the agent realizes that
the human intention may be to overtake.
For example, Fig.11 shows the control output space 4.1 s
before lane change 18, showing how the driver is going to
chose the overtake maneuver.
Fig. 10 Complex geometry at false alarm points.
Fig. 11 Detection of the intention to overtake (the camera view for this is
given by the top right frame of Fig.12).
Fig. 9. Comparison of driver and co-drover on a 10 minute course. (a, top) longitudinal dynamics (see text). (b, bottom) lateral dynamics.
PLEASE KEEP CONFIDENTIAL
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50. Incorrect interpretation
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Sensory system plays a fundamental role.
Must be accurate.
e
driver passes to edges during transitions (e.g., taking the first
exit ramp at cycle ~700). The numbered light green bands
stand for the lane changes. The interval between when the LD
motor primitive predicts the lane crossing and the actual cross-
ing of the lane is shaded. There are 21 changes correctly pre-
dicted, with anticipation ranging from 1.1 s to 2.4 s (median
1.6 s).
There are two false crossing predictions, labeled a, and b, that
happen in the non-motorway section when the trajectory pass-
es close to edges. Fig 10 shows the camera view to demon-
Fig. 10 Complex geometry at false alarm points.
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51. Missing behaviors (incomplete PA architecture), e.g., overtake maneuver.
Complex behaviors may often be decomposed in simpler ones (e.g., overtake ->
lane change + free flow + lane change)
The system in this case understands the single phase but not the complex one (it
still works!).
Inaccurate behaviors. A co-driver with non-human behaviors fails to
understand the intentions (e.g., forgetting to model under steer).
Missing hypotheses. The co-driver uses the closest hypotheses and may
fail.
Plausibility approach, together with behavioral discretization increase
robustness at the expense of granularity of intention resolution.
Behaviours from basic principles (e.g., adaptive lane keeping arise
naturally from the use of the second manoeuvre.
Discussion– Inference of intentions
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52. The system has been tested on a ~50 km road path with ordinary
drivers (24 drivers, twice each for a total of 35 hours).
False alarms were a few (2-4) per trip most due to noise in the
perception system. Very few alarms may be ascribed to incomplete/
missing/imperfectly designed co-driver behaviors (most of these
being due to mismatch between the driving styles, so not critical).
Collect data will help refine the motor primitives and behaviors built
into the system.
The hierarchical architecture is easily scalable, maintainable and
testable.
Major limitations: the system does not work yet in intersecting roads
(has poor understanding of intersecting vehicles intentions).
Discussion– User tests
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53. Technology impact on future applications

54. A co-driver that understands driver goals, is a “friend” that:
Enables Adaptive Automation (offer the appropriate support type
and level at any time as a human peer would do)
Improve execution of manoeuvres (substitute human execution with
machine execution while preserving the goal – just like chassis
control but at navigation-cognitive level)
Navigate by hints (just like a horse) and largely autonomously until
new goals come manifest form the human
Take over/supervise driver control (under certain conditions)
Is understandable to other drivers.
Unified framework for smart (safe, green, comfort) functions.
Peer-to-peer human-robot interactions
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55. Virtual drivers enables cooperative swarm behaviors.
They exchange each other goal (i.e., their Drivers ECOM states).
Inference of other agents goals can be carried out if they are not
cooperative (they also have some goal).
Safety as emergent behavior. Each agent adapts own plans to the
others, producing a collective emerging swarm behavior.
Green cooperative driving as an emerging behavior (produced by
energy efficiency criterion in the mirroring mechanisms).
Cooperative systems
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56. Novel situations happens occasionally.
Drivers are often not prepared to handle rare events, because they
have no prior experience.
Motor imagery can be used to analyze them by simulation and extend
by synthetic learning the subsumtive architecture with novel PA loops.
Accidents are rarer such events.
Co-drivers (even if they don’t survive) will collect very detailed
accident data that could be later used for synthetic learning.
Accidents (and near miss) of some co-driver will teach something to
others, which will become more and more capable of reacting
properly in rare events.
Cognitve co-driver will be able to self-extend their application
domain.
Synthetic learning for novel situations and rare
events
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57. Co-drivers will gradually collect more and more experience
This can be shared:
improved driver profiles (personas),
improved interactions,
improved handling of critical situations,
special driver classes (elder, and people with some disabilities),
naturalistic data collection,
other...
Learning interaction
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