Cooperative Autonomous Driving and Interaction with Vulnerable...

9th Workshop on Planning, Perception, and Navigation for Intelligent Vehicles. Vancouver, Canada, 24th Sept. 2017

Cooperative Autonomous Driving and Interaction with Vulnerable Road Users

Miguel Ángel Sotelo

[email protected] Full Professor

University of Alcalá (UAH)SPAIN

9th Workshop on PPNIV – Keynote

9th Workshop on Planning, Perception, and Navigation for Intelligent Vehicles. Vancouver, Canada, 24th Sept. 2017 2

u Motivation

u Autonomous Cooperative Driving

u GCDC Results

u Interaction with VRUs

u Conclusions and Future Work

Content


• Despite the great development in the past years, thereare still some major limitations in AutonomousDriving:

Motivation

Legal frameworkLegal frameworkLegal framework

NavigationNavigationNavigation ReliabilityReliabilityReliability

EfficiencyEfficiencyEfficiency Social AcceptanceSocial AcceptanceSocial Acceptance


• Navigation:- Enriched maps are needed (2 Gb/Km).

- International Consortia: BMW, Daimler, Audi (HERE).

- Online data acquisition and map building.

Limitations of Autonomous Vehicles


• Reliability:- Improvement in sensorial capabilities (adverse weather).

- Development of Cooperative Systems.

• Efficiency:- Human-like decision making and maneuvering.

- Emulation of human driving by means of prediction ofintentions of other traffic agents, such as pedestrians andother vehicles.

- There is a need for enhanced cooperation andinteraction capabilities.

Limitations of Autonomous Vehicles


u Motivation


u GCDC Results



Content


• Techniques:- Cooperation with other vehicles (autonomously or manually

driven) and with the infrastructure.

- Cooperation with VRUs (Vulnerable Road Users) byprediction their intentions and trajectories.

Autonomous Cooperative Driving

• Limitations:- Strong dependency on penetration rate.

• Goal:- Increase reliability and efficiency of autonomous vehicles.


• GCDC 2016 (three tests):- Platooning + Merging.

- Management of T-intersections.

- Management of emergency vehicles.

• Initiatives:- European Commission: funding of research projects on

Cooperative Systems and Autonomous Driving (FP7 andH2020).

- Grand Cooperative Driving Challenge (GCDC):International Competition on Autonomous CooperativeDriving in Helmond (The Netherlands) in 2011 and 2016.



• GCDC 2016: Platooning + Merging



• GCDC 2016: Management of T intersections



• GCDC 2016: Management of emergency vehicles



• DRIVERTIVE – General Architecture



• Data Fusion - Localization Example



• Communications System (ITS G5 V2V standard)

Messages types

• CAM (Cooperative Awareness Message): position, geometry andvehicles dynamics.

• DENM (Decentralized Environmental Notification Message):asynchronous messages from infrastructure o from other vehicles (e.g.emergency vehicles approaching).

• iCLCM (iGame Cooperative Lane Change Message): messages forinteraction protocol in different scenarios.



• Scenario 1: Platooning + Merging



• Scenario 1:

Platooning + Merging

Behavior on the leftlane is different fromthat on the right lane



• Scenario 2: Management of T-intersections



• Scenario 2:

T-Intersections

A safety distance mustbe kept at all times w.r.tthe preceding vehicle



Analysis of the communication channel (CCDF –Complementary Cumulative Distribution Function)



Mean and Variance of UD (Car)



Mean and Variance of UD (Truck)



Analysis of the communication channel

- For some vehicles, the probability of large delays issignificant (>10%).

- The UD degrades with distance.

- Occlusions have a strong effect on delays:

- Trucks are less occluded given that theirantennas are located at a height of 3 meters.

- Other findings: DCC in a highly congested channel is making some of the vehicles get stuck in Restrictive state and are not able to regularly access the channel.

- CAM and DENM in GCDC at 25 Hz.



Effect of UD on Emergency Braking during CACC


- There is a probability between 0.01 – 0.001 ofcollision with the leading vehicle is onlycommunications are used for CACC in a fleet of morethan 4 vehicles.

- The channel load is responsible for lowreliability.

- An additional sensor is needed (radar).


u Motivation


u GCDC Results



Content


• DRIVERTIVE – University of Alcalá’s team- Autonomous Cooperative Vehicle (Velodyne, Radar, 3D Vision, Laser,

DGPS, CANBus, Communications, fully automated)



DRIVERTIVE at GCDC 2016

GCDC Results


DRIVERTIVE – Winner of the Prize to the Best Team withFull Automation in GCDC 2016

GCDC Results


GCDC 2016

GCDC Results


u Motivation


u GCDC Results



Content


Motivation


Motivation

• Pedestrian Path Prediction in the Automotive:– Further improvement in state-of-the-art ADAS by meansof action classification

– Walking, Stopping, Starting, Bending-in

– Improvement of accuracy in 30-50 cm:

– Difference between effective and non-effectiveintervention in emergency braking systems

– Initiation of emergency braking 0.16 s in advance canpotentially reduce severity of accidents injuries by 50%

– Early recognition of pedestrians stopping actions canprovide more accurate last-second active interventions

Strong gains are expected in the performance and reliability of active

pedestrian protection systems






Proposed Approach

Global Scheme

Off-line Motion Capture System

Recovery of 3D pose and position

Lateral predicted position

On-line

Probabilistic Training

Knowledge of Pedestrians dynamics

Stereo Cameras

Transformation to latent space +

prediction

Geometric processing

3D Pedestrian Pose

Estimation


Pedestrian Pose Measurement

Pedestrian Skeleton considered in this research



Method for Joints Extraction - Example



Method for Joints Extraction – Results



Body parts detection using Deep Learning


General Method - Overview


Activity Recognition - Example


Experimental Results

Detection Delay - Summary



Probabilistic Action Classification



Video sequence showing prediction results


Intelligent Interface with VRUs

GRAIL – GReen Assistant Interfacing Light


u Motivation


u GCDC Results



Content


Conclusions and Future Work

Conclusions- Autonomous Cooperative Systems will pave the way to the massive

and robust deployment of self-driving cars.

- The V2V communication link is still a weakness that needs further attention from the scientific community in order to provide real-time and robust communication capability among large fleets of vehicles.

- Anticipating the intentions of other traffic participants, such as VRUs and vehicles, is essential for mimicking human drivers behavior.

Future Work- Enhancement of V2V communication channel for large fleets of

vehicles (antenna placement, frequency of data, etc.).

- Context-based action prediction using Probabilistic Graphical Models (Bayesian Networks) is under development for VRUs and vehicles.

- Gaze direction, group behavior.


Cooperative Autonomous Driving and Interaction with Vulnerable Road Users

Thanks for your kind attention!

Miguel Ángel Sotelo

[email protected] Full Professor

University of Alcalá (UAH)SPAIN

9th Workshop on PPNIV – Keynote

Cooperative Autonomous Driving and Interaction with Vulnerable...

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