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ADAS&ME Presentation - H2020 ERTRAC
1. ADAPTIVE ADAS TO SUPPORT INCAPACITATED
DRIVERS MITIGATE EFFECTIVELY RISKS THROUGH
TAILOR MADE HMI UNDER AUTOMATION
This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 688900
Dr. Anna Anund
Coordinator ADAS&ME
2. ADAS&ME VISION
28/11/18 3
Develop Advanced Driver Assistance Systems (ADAS) that incorporate
driver/rider state, situational/environmental context and adaptive
interaction, to automatically transfer control between vehicle and
driver/rider and thus ensure safer and more efficient road usage for all
vehicle types (conventional and electric car, truck, bus, motorcycle)
ADAS&ME VISION
4. ADAS&ME IMPACT
528/11/18
Objectives & Status
• An adaptive architecture and technical implementation
for enabling automation with driver/rider state input.
• Development of robust detection/prediction algorithms
for driver/rider state monitoring adaptive to each of the
seven UC.
• Development of multimodal adaptive HMI and
personalised driver/rider behaviour profiles,
considering inter-individual differences and
environmental context.
• Performance of targeted tests for the selection of HMI
elements that optimally support each UC.
• Adaptation of existing EuroNCAP test protocols from
non-automated to automated driving modes.
• Evaluation of the developed systems and use cases with
a wide pool of drivers/riders with real vehicles on real
roads.
• ADAS&ME Adaptive System Architecture developed
(D2.1).
• Data collection for all targeted driver states
performed (A4.2/ A4.3/ A7.2). Algorithms
development is almost ready.
• HMI Framework ready & first prototypes in progress
(A5.1/ A5.2). Ident./person. module design ready,
implementation in progress (A4.6/ A5.3). ESAS
components ready (D3.1/D3.3).
• HMI optimization tests almost ready.
• Final evaluation scenarios drafted and tests planned.
Tests planned in 2019.
• Final evaluation timing per UC ready. Tests planned in
2019.
….
….
5. FOCUS ON SEVEN USE CASES
6
ATTENTIVE
LONG HAUL
TRUCKING
ELECTRIC VEHICLE
RANGE ANXIETY
DRIVER STATE-
BASED SMOOTH &
SAFE AUTOMATION
TRANSITIONS (CAR)
NON-REACTING
DRIVER EMERGENCY
MANOEUVRE (CAR)
LONG RANGE
ATTENTIVE
TOURING WITH
MOTORBIKE
RIDER FAINT PASSENGER PICK
UP/DROP OFF
AUTOMATION FOR
BUSES
28/11/18
FOCUS ON SEVEN USE CASES
11. ENVIRONMENTAL CONTEXT MONITORIN
12
HD Map geometry
Traffic flow
Weather
Vehicle sensing
28/11/18
ENVIRONMENTAL CONTEXT MONITORING
– A3.1 analysed broader changes in the surroundings
Result: Driving task intensity indicator for road
ahead
– A3.2 focused on local sensing
Result: Probabilistic positioning estimation and
extended sensor perception via connectivity
– A3.3 interprets the situation
Result: Utilizing results of A3.1 and A3.2, assesses
the situation to derive situation criticality
A3.2 / D3.1 & D3.3
A3.3 / D3.2 & D3.4
A3.1 / D3.1 & D3.3
12. DRIVER/RIDER STATE MONITORING
13
Fatigue, Sleepiness, Stress, Inattention, Anxiety, Fear,
Thermal fatigue and Faint.
Algorithms based on existing data, with
complementary data collections - (Stage I and Stage II)
Personalisation: driver identification module and
personalised HMI
28/11/18
Long haul trucking - Rest
Indicators – driver state
Messages and middlewareAutomated docking at bus
stop – inattention/ sleepiness
Driver state based smooth and safe
transition – stress, distraction and
emotion
DRIVER/RIDER STATE MONITORING
13. HUMAN-MACHINE INTERACTION FAMEWORK
14
• Generic framework for HMI – adaptive per UC
• Iterative tests for each UC with VR and simulators
28/11/18
HUMAN-MACHINE INTERACTION FRAMEWORK
14. 15
Final evaluations and demonstration in Barcelona – Public deliverable with all
tests and their design and protocols.
Caption:
0-General Road
1-High speed Track
2- Noise Track
3-Fatigue Track/Comfort Track
4-Dynamic Platform A
5-Dry Handling Circuit/ Dynamic Platform C
6-Test Hills
7-Straight Line Braking Surfaces/
Comfort Track and SIM city
8-Dynamic Platform B
9-Off road/Forest Track
10-Wet Circle
11-Wet Handling Circuit
28/11/18
TESTING AND EVALUATIONSTESTING AND EVALUATIONS
UC
M36 M37 M38 M39
W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4
A
B
C&D
E&F
G
15. ADAS&ME IMPACT
16
• 3 areas of impact
Safety, Mobility and
Environment
Economic and social
Legal and Regulatory
• Based on data from evaluation
and market analysis
28/11/18
IMPACT
16. DISSEMINATION ACHIEVEMENTS
1826/11/18
ADAS&ME has organized international workshops and
has participated in key events around the world:
More than 10 papers has been published and a
number of demonstrations have been developed…
Anna is a truck driver. Autonomous driving will enable her to relax during her long working hours and ensure that she is more refreshed when she needs to take over the driving task. Her truck communicates with her when it is time to hand over control to her.
Paul is driving an electric car. He feels anxious about whether he will reach his destination within the range of the car. By means of information visualisation and rerouting propositions provided by the car, Paul will arrive safe and relaxed at his destination.
Niki, a car driver, is offered the option of automated driving when the vehicle detects she is stressed and sleepy. When Niki is again deemed fit to drive, the vehicle will ask if she wishes to take back control.
In some situations, Niki may not be able to drive or react in a safe way. The vehicle will then automatically take over control and handle the situation safely.
Bruno is riding a motorcycle capable of checking whether he’s impaired (e.g. stressed) by monitoring his body. His vehicle is able to warn him and propose a suitable rest stop, or even limit its own performance, to help ensure that Bruno will arrive safely at his destination.
Tania is riding a motorcycle during a very hot day. Her motorcycle detects her “unfit” state and proposes that she take a rest. Tania is not responsive, so her motorcycle limits its performance, turns on the hazard lights, decelerates automatically and assists her until it stops on the side of the road.
Peter is driving a bus and would benefit from support at the bus stop during a stressful and safety-critical situation. At the bus stop, the system takes over and Peter is able to relax and focus on his passengers. Leaving the bus stop, the system checks that Peter is fit to drive and hands back control to him.