technical and legal challenges for urban autonomous...

Technical and Legal Challenges forUrban Autonomous Driving

Seung-Woo Seo, Prof. Vehicle Intelligence Lab.Seoul National Universitysseo@snu.ac.kr

I. Main Challenges for Urban Autonomous DrivingI. Dilemma in Autonomous Driving

II. Approach to Human‐like DrivingI. Intention‐Aware Decision MakingII. Imitation Learning

III. Autonomous Driving Research in SNUI. Demonstration of SNUver

IV. Conclusion

2

Challenges for Urban Autonomous Driving

Considerations for Urban Autonomous Driving

Moving & static objects• Pedestrians• Other vehicles• Traffic light & signs• Unforeseen events

Crossing intersection Turning Lane changes Parking Entering and exiting drop off stations Etc.

First Self-driving in City Road in Korea(2017. 6. 22)

Yeouido Area in Seoul

Demonstration at Yeouido Area in Seoul

7

Driving course on Yeuido

5

4

3

2

1

6

7

Lane-change in heavy traffic

Crossing a double-yellow line to passby an illegally parked car

In urban environments, dilemma situations frequently occur

Decisions at a yellow traffic light

8

Dilemma in Autonomous Driving

9

Dilemma in Autonomous Driving

I. Legal aspect

II. Interactivity aspect

III.Technology aspect

3 Different Aspects

Legal Aspect

Crossing a double-yellow line to pass by an illegally parked car

VS.Crossing a double-yellow line

illegal & socially compliant decision

Waiting until an illegally parked car leaves

legal & impractical decision

“AV violating the traffic law”

Interactive driving (ex. Lane cut‐in)

‐12‐

Interactivity Aspect

13

Human-Like Driving

Dilemma in Autonomous Driving

I. Legal aspectEX) Crossing a double‐yellow line to pass an 

illegally parked car

II. Interactivity aspectEX) Lane‐change in heavy traffic

unsignalized intersection

III.Technology aspect

3 Aspects

Approach to Human-Like Driving

15

TASK 1. LANE‐CHANGE IN HEAVY TRAFFIC TASK 2. INTERSECTION TASK N. HIGHWAY

Single‐Task Policy 1

Policy Optimization

Single‐TaskPolicy 2

Policy Optimization

Single‐TaskPolicy N

Policy Optimization

Model for Decision Making

17

1tX

1tY

1t

A

R1tO tO

tY

t

tX

A

R

The state space “S” is a joint space : Ego-vehicle’s state space

: Other vehicles’ state space

: Other vehicles’ driving intention

The action space “A” : A = . , . , .

The reward model Very high penalty when vehicle is predicted

to collide. Very high reward when vehicle arrives at its goal. Low penalty when vehicle moves at each step

Passing through intersection as fast as possible without any collision

Θ ,

, ,

, ,

Experimental Environment

18

18

SNU Campus roadTotal length : ~4km

행정대학원

국제대학원

기숙사삼거리

대운동장자동화

시스템

연구소

Start

Goal

Learning from Expert Drivers• Expert drivers understand human interactions on the road and comply with mutually accepted rules, which are learned from countless experience

Brenna D. Argall, at el. “A survey of robot learning from demonstration”, Robotics and Autonomous Systems 57 (2009): 469‐483

Behavior Cloning Inverse Reinforcement Learning

Learning Technique

PolicyDerivation

Learning Technique

, , ,

Mapping from states to actions(Supervised Learning) Reconstruct reward function

19

Imitation Learning

Driving dilemma in single lane road• Crossing a double-yellow line to pass by an illegally parked car

Demonstration of expert drivers

Sang‐Hyun Lee and Seung‐Woo Seo, “A Learning‐Based Framework for Handling Dilemmas in Urban Automated Driving”, IEEE International Conference on Robotics and Automation(ICRA), 2017 20

Imitation Learning

Experimental Environments

21

SNU Campus roadTotal length : ~4km

Imitation Learning

Autonomous Driving Research in SNU

[November 19, 2013]Grand Prize in unmanned self‐driving car contest

[November 4, 2015]Driverless taxi on 

SNU Campus

[November 15, 2016]Door‐to‐Door Automated Driving on SNU Campus

[June 22, 2017]Automated Driving inUrban Environments

23

SNUverSNU Automated Drive

SNUver 1 (2015)

SNUver 2 (2016)

SNUvi (2017)

Discussed several key issues related to dilemma in urban autonomous driving Briefly introduced our learning-based approaches to

human-like driving There still remain many challenges that make the urban

autonomous driving very hard

Future Work

27