priority programme 1835 cooperative interacting automobiles … · 2019-08-07 · overview focus...
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Priority Programme 1835
Cooperative Interacting Automobiles
2018 C. Stiller
Deep Learning, Behavioral Safety and
Motion Planning
Overview
Focus Programme „Cooperative Interacting Automobiles“
Constraints on Motion Plans
Drivability
Integrity (even when you do not know the plans of others)
Legality
Comfort (soft)
Optimization Based Motion Planning
Probabilistic Models and Planning
MDP
POMDP
Learning
RL
DL
Behavioural Safety
German Science Foundation
Focus Research Programme
Cooperatively Interacting Automobiles
Christoph Stiller, Karlsruhe; Autonomous Automobiles (Spokesman)
Wolfram Burgard, Freiburg; Robotics
Barbara Deml, Karlsruhe; Ergonomics
Lutz Eckstein, Aachen; Vehicular Technology
Frank Flemisch, Aachen; Ergonomics
Frank Köster, Braunschweig; Intention Recognition
Markus Maurer, Braunschweig; Representation of Skills
Gerd Wanielik, Chemnitz; Situation Understanding
Cooperatively Interacting
Automobiles
~30 Ph.D. students across Germany
Automatic driving & car2x
yield new opportunities of automated
interaction
Research on added value of automated
cooperation between automated vehicles
and other traffic participants
Research Issues
How should interaction between automated vehicles and other
traffic participants be implemented in a cooperative way?
Impacts on traffic. Will novel forms of traffic operation arise?
Explicit & implicit cooperation
Mixed traffic & automated-only traffic scenarios
Towards Cooperative Interaction
assessment
&
plausibility
checking
cooperative perception
data and
information
base
cooperative maneuver
and trajectory planning
ego
sensors
driver
vehicle internal
situation prediction and
intention recognition
sensors
of other
traffic
participants... car2x
negotiation and information
exchange with others
information
to other
traffic participants
exchange with
other traffic
participants
Types of Cooperation
pedestrians
conventional vehicle
with driver
assisted vehicle
with driver
automated vehicle
without driver
explicit cooperation
implicit cooperation
Ego-Fzg.
Automated vehicles may cooperate
with:
other automated vehicles
assisted vehicles or vehicles with a
human driver
other traffic participants
pedestrian, passenger in Ego-Veh.
Communication
explicit (car2x, …)
implicit
Research Fieldsa) Cooperative Perception
Implement a ‘telematic perception horizon‘ through exchange of
information with others
Issues: latencies, uncertainties in spatio-temporal
acquisition, perception loops caused by cyclic information
exchange, etc.
b) Situation Prediction
Predict behavior and trajectories of others (vehicles, humans,
…) to enable cooperation
Create predictable ego-behavior
Research Fieldsc) Cooperative Maneuver and Trajectory Planning
Cooperative trajectory planning of automated vehicles with
other traffic participants
Implicit or explicit coordination of different maneuver options
d) Data and Information Base (‘crowd mapping’)
Knowledge aggregation in a collektive data and information
base.
Focus on information that influences tactical driving
decisions
Research Fieldse) System ergonomics
Make automated decisions transparent and acceptable to
humans (inside and outside the vehicle)
Generate implicit cooperation with pedestrians, byciclist, etc.
Interaction between passenger and automated vehicle
f) Architecture of cooperatively interacting automobiles
Metrics for quality assessment of provided information, of
cognitive skills, of safety of trajectories, …
…
Introduction
steering angle acceleration
map goalpose objects
Motion
Planning
dynamics rules
Human Behavior Model
Information Decision Planning Navigation
Recognition Assoziation Rules Guidance
knowledge based behavior
rule based behavior
Feature
Extraction
Signal-Reactive
SkillsStabilization
skill based behavior
Sensor
Information
Subcortical
Information
Motoric
Action
cf. Rassmusen 1983
Constraints on Motion Plans
Hard Constraints
Drivability
Integrity
Static Obstacles
Dynamic Obstacles
(some of which may be difficult to predict)
Traffic Rules (sometimes vague)
Soft Constraints
Comfort
Predictability
Cooperativity
Vehicle Dynamics
Friction Circle
Assume limit for force acting on vehicle (or wheel) by friction coefficient
and vertical force .
maximumlongitudinal accelerationmaximum
deceleration
maximumlateral acceleration
Acceleration vectorsbeyond friction circleare not feasible
friction circle longitudinal and lateral
dynamics are coupled !
on dry road
lateral force over wheel slip angle
for varying longitudinal force FxLangitudinal
wheel axis
lower potential for lateral force due to longitudinal force Fx
Acceleration in narrow curves changes path
linearized wheel forces front/rear:
cornering stiffness
Wheel Forces
~10 °Wheel slip angle
late
ral fo
rce
FS
Bicycle Model
Models major dynamic properties of a vehicle
Replaces wheels of each axle by a single centered
wheel
2d model
Dry road, moderate lateral acceleration < 4m/s²
Constant velocity
Variants:
Kinematic bicycle model
-> for low speed, neglect lateral tire forces
Kinetic bycicle model
Non-linear
linearized
CoG
v
Kinematic Bicycle Model Ackermann Steering Angle
CoG
invariant
point
• neglect lateral forces
• valid for slow driving
22tan
hM
A
lr
l
M
Ar
ltan
for :,1 22
MhA rl
A
Mr
Kinetic Bicycle Model
CoG
invariant
point
Mr
Collision Avoidance
Configuration Space (C-Space)
The Configuration Space C is the set of all admissible states
(configurations) q of a robot
Defines the set of feasible robot parameters and thus the search
space for planning
Free Space: Point Robot
Cfree := {set of robot parameters q that is collision free}
For a point Robot: The space in R² not occopied by an obstacle
[Figs.: H. Choset,
http://www.cs.cmu.edu/~motionplanning/]
Free Space: Circular Robot
extend obstacles by robot radius
= convolute robot shape with obstacle map
Thao
Dang,
23
Free Space: Arbitrarily-Shaped Robot
Orientation matters
3d configuration space
for equivalent point robot
Free Space: Arbitrarily-Shaped Robot
25
Fast Collision Checking
[Ziegler et al. 2011]
Approximation of vehicle shape by a set of circles
Motion Planning
Motion Planning Methodology
Roadmaps• Visibility Graphs
• Voronoi-Diagrams
Discrete Graph Search• Dijkstra
• A*
• D*, D* Lite
Monte-Carlo-Methods• Probabilistic Roadmaps
• Rapidly Exploring Random Trees
Potential Field Methods• Artificial Potential Fields
• Elastic Bands
• Vector Field Histograms
Continuous Optimization• Parameter Optimization of Motion
Primitives
• Model Predictive Control MPC
Learning Methods• Reinforcement Learning
• Inverse Reinforcement Learning
• Deep Learning
• Deep Driving
Trajectory Representation for Planningglobal, discrete,
combinatoric
[Ziegler et al.2009–2011] [Ziegler et al.2011–2014]
local, continuous, variational
Continous Planning
Model Predictive Control
Optimal Control
Find the control minimizing cost integral J
under the vehicle dynamic constraints
and the equality and inequality constraints
Optimal Control
If J is quadratic and f,g,h are linear, the optimization is efficiently done
by a quadratic program
Fast constant time solvers exist for quadratic programs
For the kinematic bicycle model, x is a flat output, i.e.
Model Predictive Control (MPC)
Plan the optimal control for a time horizon T and conduct this control
input for a short time interval
After each time interval plan again for horizon T and conduct the new
control input for
In the extreme case of infinite T, u should not change unless the
optimal control problem changes!
Example: 1D MPC Lateral Control
Let the velocity of the vehicle be given in x-direction
Thus trajectory planning is reduced to the lateral component
Let further
and y be constrained by road boundaries (or obstacles) with linear h
yields a quadratic program :)
left border
right border
MPC 1D Trajectory example
left border right border
unconstrained
constrained
Dynamic Objects
We need to plan for ourselves …
… and for others
2d Trajectory planning
optimize cost functional
outer conditions
enforce integrity,
e.g.:
inner conditions
enforce drivability,
e.g.:
subject to hard conditions
2d Trajectory planning
Non-quadratic programs ;( result due to
x-y coupling due to vehicle dynamic model
x-y decoupling (sacrifizing optimality) yields piecewise
quadratic programme in many situations
non-linear vehicle dynamics
obstacles whose trajectory depends on ours
much slower iterative solutions
optimality cannot be guaranteed by solver.
in general, solution depends on initialization
MPC Results
0 10 20 30 40 50
-25
-20
-15
-10
-5
0
5
10
15
1 sec
2 sec
3 sec
MPC Results
-20 -10 0 10 20 30 40 50 60 70
-10
0
10
20
30
40
50
60
Lanelets
• left & right boundary
• reference trajectory
of driving corridor
Ego-position
& past 2 poses
Static obstacle
@[t1,te]
crossing
pedestrian
Oncoming vehicle
MPC 2D Trajectory planning
Heidelberg
Feudenheim
Pedestrian Protection
Courtesy Daimler AG
Maneuver Planning
Maneuver Planning
Idea:
Decouple maneuver and trajectory planning
Initialize iterative trajectory planner with a trajectory in the same
maneuver class (homotopy class)
MPC 2D Trajectories
P-Map(lanelets,
local rules),
L-Map(Localisation)
3 2
10
Perception PredictionDetermine
Maneuver
VariantsDecide best
Maneuver &
Trajectory
Trajectory
Planner for
all Variants
MPC Trajectories for each Maneuvre
Homotopy
time
[Bender et al. ITSC 2015]
Probabilistic Models and Planning
Effects of Uncertainty
Probabilistic Models
Deterministic models do not consider uncertainty appropriately:
Uncertainty in actuation
Uncertainty in sensing
Uncertainty in occluded regions
Uncertainty in intent and future trajectories of other traffic
participants
Effects of Uncertainty
Assuming certainty: yes
Can my black vehicle safely pass the white one?
Effects of Uncertainty
Assuming uncertain perception
in position and velocity:
Can my black vehicle safely pass the white one?
too tight
Effects of Uncertainty
The situation gets even worse with
uncertain prediction
Can my black vehicle safely pass the white one?
In this consideration overtaking is never possible
when the prediction horizon is large enough
Effects of Uncertainty
Considering uncertainty:
Can my black vehicle safely pass the white one?
Just take a few safe steps and see what happens
Effects of Uncertainty
Considering uncertainty:
Can my black vehicle safely pass the white one?
Just take a few safe steps and see what happens
Effects of Uncertainty
Considering uncertainty:
Can my black vehicle safely pass the white one?
Just take a few safe steps and see what happens
Effects of Uncertainty
Considering uncertainty:
Can my black vehicle safely pass the white one?
Just take a few safe steps and see what happens
Effects of Uncertainty
Considering uncertainty:
Can my black vehicle safely pass the white one?
Just take a few safe steps and see what happens
Effects of Uncertainty
Considering uncertainty:
Can my black vehicle safely pass the white one?
Just take a few safe steps and see what happens
Effects of Uncertainty
Considering uncertainty:
Can my black vehicle safely pass the white one?
Well, but what if …
Effects of Uncertainty
Considering uncertainty:
Can my black vehicle safely pass the white one?
Well, but what if …
=> replan, upon unexpected evolution
Probabilistic Trajectory Planningpose = (position, orientation)
trajectory
past
future
special case „certain prediction“, e.g. through v2v communication
Interaction Modes
Yielding:
Cooperation: Yielding:
Yielding:
Cooperation:
Yielding:
Cooperation:
Cond. independent
3 2
10
Markov Decision Process
Policy decides action a in state x
Reinforcement Learning = policy learning
maximizing expected future reward
Even under mild assumptions
- deterministic future
and coarse quantization of a and x
An exhaustive search is infeasible for real time driving
Shalev-Swartz et al. propose abstraction of the action space to
„semantic action“ ~ maneuver ~ homotopy
Partially Observable Markov Decision
ProcessIn a POMDP, states are not directly observable, but are observed via
a probability distribution
The policy then perates on these observations
State transition distribution
[Hubmann&al IV 2017]
POMDP Planning
[Hubmann&al IV 2017]
Define state space
POMDP Planning
[Hubmann&al IV 2017]
Observations
POMDP Motion Planning
[Hubmann&al IV 2017]
POMDP Planning
[Hubmann&al IV 2017]
Approximate Q function with Monte Carlo Sampling
POMDP Planning
[Hubmann&al IV 2017]
Results
POMDP Planning - Results
[Hubmann&al IV 2017]
POMPD predicts “when it can decide” rather than the decision itself!
Learning
Neural Networks as Graph Solvers
[RehderWirthLauerStiller 2017]
KIT – The Research University in the Helmholtz Association
www.kit.edu
Deep Graph Solvers
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
StartGoal
Find shortest path from start to goal:
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
0
Start
?
??
?
?
Goal
Find shortest path from start to goal:
Assign edge costs, node costs, Start = 0
4
2
1
4
5
4 2
2
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
0
Start
4
2?
?
?
Goal
Find shortest path from start to goal:
Assign edge costs, node costs, Start = 0
Propagate and sum costs
4
2
1
4
5
4 2
2
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
0
Start
4
27
6
?
Goal
4
2
1
4
5
4 2
2
Find shortest path from start to goal:
Assign edge costs, node costs, Start = 0
Propagate and sum costs
Expand cheapest node
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
0
Start
4
27
6
?
Goal
4
2
1
4
5
4 2
2
Find shortest path from start to goal:
Assign edge costs, node costs, Start = 0
Propagate and sum costs
Expand cheapest node
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
0
Start
4
27
6
?
Goal
4
2
1
4
5
4 2
2
Find shortest path from start to goal:
Assign edge costs, node costs, Start = 0
Propagate and sum costs
Expand cheapest node
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
0
Start
4
27
5
?
Goal
4
2
1
4
5
4 2
2
Find shortest path from start to goal:
Assign edge costs, node costs, Start = 0
Propagate and sum costs
Expand cheapest node
Re-assign minimum cost
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
0
Start
4
27
5
7
Goal
4
2
1
4
5
4 2
2
Find shortest path from start to goal:
Assign edge costs, node costs, Start = 0
Propagate and sum costs
Expand cheapest node
Re-assign minimum cost
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
0
Start
4
27
5
7
Goal
4
2
1
4
5
4 2
2
Find shortest path from start to goal:
Assign edge costs, node costs, Start = 0
Propagate and sum costs
Expand cheapest node
Re-assign minimum cost
Trace back shortest path
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
Find shortest path from start to goal:
Start
Goal
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
Find shortest path from start to goal:
Graph Edges
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
Find shortest path from start to goal:
Obstacle
[Eike Rehder 2017]
Short Review: Dijkstra‘s Algorithm
Find shortest path from start to goal:
[Eike Rehder 2017]
Shortest Path with a CNN
[Eike Rehder 2017]
Finding the Shortest Path with a CNN
Assign edge costs, node costs, Start = 0
[Eike Rehder 2017]
Finding the Shortest Path with a CNN
Assign edge costs, node costs, Start = 0
Propagate
[Eike Rehder 2017]
Finding the Shortest Path with a CNN
Assign edge costs, node costs, Start = 0
Propagate
=
[Eike Rehder 2017]
Finding the Shortest Path with a CNN
Assign edge costs, node costs, Start = 0
Propagate and sum costs
= + 1 =
[Eike Rehder 2017]
Finding the Shortest Path with a CNN
Assign edge costs, node costs, Start = 0
Propagate and sum costs
=
+1
+1
+1
+1
+inf
[Eike Rehder 2017]
Finding the Shortest Path with a CNN
Assign edge costs, node costs, Start = 0
Propagate and sum costs
=
[Eike Rehder 2017]
Finding the Shortest Path with a CNN
Assign edge costs, node costs, Start = 0
Propagate and sum costs
Re-assign minimum cost
= min
[Eike Rehder 2017]
Finding the Shortest Path with a CNN
Assign edge costs, node costs, Start = 0
Propagate and sum costs
Re-assign minimum cost
= min
[Eike Rehder 2017]
Finding the Shortest Path with a CNN
CostNon-Zero
Padding (!)
Transition
Filters
Transition
Cost
+
Cost
per
Action
min
pool
Updated
Cost
Replace
[Eike Rehder 2017]
Finding the Shortest Path with a CNN
CostNon-Zero
Padding (!)
Transition
Filters
Transition
Cost
+
Cost
per
Action
min
pool
Updated
Cost
Replace
Argmin of this layer is transition policy
[Eike Rehder 2017]
Evaluating the Shortest Path with a CNN
+
*
Current
State
Transition
Policy
Transition
Selection
Flipped
Transition
Filters Next
State
argmin
Destination
State
[Eike Rehder 2017]
Example I:
Path Planning
[Eike Rehder 2017]
Example I: Path Planning
Start
Goal
Obstacle
Find shortest path from start to goal:
[Eike Rehder 2017]
Nine possible transition filters
Cost is the traversed distance
Example I: Path Planning
+1 +inf
+√2
+1
+1
+1 +√2
+√2 +√2
[Eike Rehder 2017]
Example I: Path Planning
Cost ModelAdditive layer
High cost where obstacle is located
[Eike Rehder 2017]
Example I: Path Planning
Cost Map State Transition Map
[Eike Rehder 2017]
Finding the Shortest Path with a CNN
If you use Dijkstra:
Graph traversal with known transitions is faster
States can be updated selectively
Visited nodes will never be touched again
Why would you do it then?
[Eike Rehder 2017]
Example II:
Imitation Learning
[Eike Rehder 2017]
Example II: Imitation Learning
Arial view: Google Maps
Intersection in Karlsruhe
[Eike Rehder 2017]
Example II: Imitation Learning
Recorded trajectories
Teach a network to imitate human behavior
Intersection in Karlsruhe
Arial view: Google Maps
[Eike Rehder 2017]
CostNon-Zero
Padding (!)
Transition
Filters
Transition
Cost
+
Cost
per
Action
min
pool
Updated
Cost
Replace
Example II: Imitation Learning
[Eike Rehder 2017]
Example II: Imitation Learning
CostNon-Zero
Padding (!)
Transition
Filters
Transition
Cost
+
Cost
per
Action
min
pool
Updated
Cost
Replace
Fill in the whole bunch of CNN techniques
[Eike Rehder 2017]
Example II: Imitation Learning
+
In our case:
FCN operating
on the arial view
[Eike Rehder 2017]
Example II: Imitation Learning
Path driven by human
[Eike Rehder 2017]
Example II: Imitation Learning
Path driven by human Cost map from arial image
[Eike Rehder 2017]
Example II: Imitation Learning
Path driven by human Cost map after planning
[Eike Rehder 2017]
Example II: Imitation Learning
Path planned by network Cost map after planning
[Eike Rehder 2017]
Example II: Imitation Learning
Path planned by network
Path driven by human
Cost map after planning
[Eike Rehder 2017]
Markov Decision Processes as
Deep NN
[Eike Rehder 2017]
Markov Decision Processes as CNN
Camera image
Top view and semantic map
Road
Sidewalk
Obstacles
[Eike Rehder 2017]
Markov Decision Processes as CNN
CostNon-Zero
Padding
Transition
Filters
Transition
Cost
+
Cost
per
Action
min
pool
Updated
Cost
Replace
[Eike Rehder 2017]
Markov Decision Processes as CNN
Cost
Value Non-Zero
Padding
Uncertain
Transition
Filters Transition
Cost
Reward
+
Cost
Reward
per
Action
min
max
pool
Updated
Cost
Value
Replace
Shankar et al. „Reinforcement Learning via
Recurrent Convolutional Neural Networks “, ICPR 2016
[Eike Rehder 2017]
Example III:
Pedestrian Prediction
[Eike Rehder 2017]
Example III: Pedestrian Prediction
Camera image
Semantic map and top view
Teach a network to predict human motion by planning
Road
Sidewalk
Obstacles
[Eike Rehder 2017]
Example III: Pedestrian Prediction
Crop of map centered
around pedestrian
Road
Sidewalk
Obstacles
Pedestrian
[Eike Rehder 2017]
Example III: Pedestrian Prediction
Predict destination for planning
Road
Sidewalk
Obstacles
Pedestrian
[Eike Rehder 2017]
Example III: Pedestrian Prediction
Predicted with MDP Net
Road
Sidewalk
Obstacles
Pedestrian
[Eike Rehder 2017]
Deep Learning of Motion Trajectories
[RehderWirthLauerStiller 2017]
Value Iteration Network
Policy Learning in a
Markov Decision Process
Motion Generation
applying the learned Policy
[RehderWirthLauerStiller 2017]
The People
Marcos Sobrinho
Learning to Plan
Florian Wirth
Destination Prediction
Philipp Bender
Learning to Plan
Jannik Quehl
Trajectory Data
Sahin Tas
Trajectory Data
Haohao Hu
Trajectory Data
Eike Rehder
Trajectory Learning
[Eike Rehder 2017]
Behavioural Safety
Functional Evolution or Disruptive Change?
Self
Driving
Car
SAE Automation Levels
L0 L1 L2 L3 L4 L5
Driver
completely in
control
No automated
intervention
Driver only
Driver
permanently in
control for
some
functions
Single control
functions such
as speed
selection,
braking or lane
keeping are
automated
Assisted
Driver
permanently
monitors
function and
environment
Vehicles
performs
longitudinal
and lateral
control in
defined use
case
Partial Automation
Driver must
always be
available to
resume control
within
reasonable
time
Vehicles
performs
longitudinal
and lateral
control in
defined use
case. At limits
it requests
driver to
resume driving
with sufficient
time margin
ConditionalAutomation
Driver is not
required
during defined
use case
Vehicles
performs
longitudinal
and lateral
control in
defined use
case
HighAutomation
Manual
Driving
Adaptive
Cruise Control
Stop & Go
Automation
Highway
PilotLast Mile
Taxi
No driver
required
Vehicles
performs
longitudinal
and lateral
control in all
situations
FullAutomation
Self-Driving
Car
First Deadly Accident of Automated Car March 18, 2018, Tempe, AZ, USA
~1 sec before impact
Safety Goal
Legal Risk and Safety
Risk is the potential of losing something of value.
Safety is the absence of risk
Hence, safety is a binary measure: a system is either safe or not.
Bayesian Risk
Sum of probability of each event x hazard (cost) of the event
Events could be accidents classified by Abbreviated Injury Scale (AIS)
For simplicity we restrict the following considerations to fatalities.
Safety GoalToday, about 1.25 Million persons suffer a fatal accident in road traffic
per year.
In industrial countries the risk to suffer a fatal accident in road traffic is in
a similar range, e.g. for conventional driving in Germany
Automated Driving is expected to reduce this number significantly.
On the flip side AD is a new technology and is exposed to the risk of
every new technology that extends over todays technology frontiers.
Automated Driving is a risky technology that is expected to improve
safety. A careful safety assessment is essential prior to market
introduction.
Accidents of
Conventional
Driving
Accidents of
Automated
Driving
How to predict newly introduced accident
types of automated driving?
Safety GoalWhat is an acceptable risk?
Naive Thinking: The safety goal for SDA should be „Zero Accidents“
However, many traffic accidents are unavoidable by an SDA.
Hence the safety goal should be „safe as many as possible“, i.e.
maximize SIF
How much larger than 1 must SIF be for market introduction?
It should be significant as the risk exposure is more difficult to control
by individuals through safe traffic behavior.
This value should be a societal consensus.
Empirical Determination of SIF
From [Wachenfeld 2017]
Poisson distribution
𝜆: Expected number of
events during experiment
Empirical Determination of SIF
From [Wachenfeld 2017]
For a system which has SIF = 2
one needs to test for 6 x 2 10^8 km
in order to expect verification of SIF > 1
with 95% confidence
Hence empirical proof of safety
requires large scale deployment
(1)Wachenfeld, W., Winner, H.: Die Freigabe des autonomen Fahrens. In: Maurer, M.,
Gerdes, J.C., Lenz, B., Winner, H. (Hrsg.) Autonomes Fahren, pp. 439-464. Springer
Berlin Heidelberg (2015)
(2)Winner, H.: ADAS, Quo Vadis?, in Winner, H.; Hakuli, S.; Lotz, F.; Singer, C. (eds.):
Hand of Driver Assistance Systems, Springer 2016
(3)Statistisches Bundesamt / German Federal Statistical Office, 2014,
https://www.destatis.de/DE/Publikationen/ Thematisch/TransportVerkehr/
Verkehrsunfaelle/VerkehrsunfaelleJ2080700147004.pdf?__blob=publicationFile
(4)Wachenfeld, Walther: How Stochastic can Help to Introduce Automated Driving,
Dissertation Technische Universität Darmstadt, http://tuprints.ulb.tu-
darmstadt.de/5949 (2017)
(5)Schuldt, F.; Saust, F.; Lichte, B.; Maurer, M.; Scholz, S.: Effiziente systematische
Testgenerierung für Fahrerassistenzsysteme in virtuellen Umgebungen, in:
Automatisierungssysteme, Assistenzsysteme und eingebettete Systeme für
Transportmittel (AAET), Braunschweig, 2013
References for Safety
Further Reading
Steven M. LaValle: Planning Algorithms,
http://planning.cs.uiuc.edu/
Choset et. al.: Principles of Motion Planning, MIT Press.
Latombe’s “motion planning” Lecture,
http://robotics.stanford.edu/~latombe/cs326/2007/index.htm
The Open Motion Planning Library (OMPL),
http://ompl.kavrakilab.org/
Robot Operating System (ROS); http://www.ros.org
Further Research
Collaborate with us
Without financial support from us for limited time:
Guest Scientist for limited time
Master student, PhD student with support of the supervisor
Postdoc, Prof.
With financial support
PhD student
Postdoc
Send your application to [email protected]