Imitation Learning for Vision-based Lane Keeping Assistance ITSC’17 Workshop on Deep Learning for Autonomous Driving

Christopher Innocenti∗ , Henrik Lind´en∗ , Ghazaleh Panahandeh∗ , Lennart Svensson† , Nasser Mohammadiha∗† October 16-19, 2017 - Yokohama, Japan Zenuity AB∗ , Chalmers University of Technology† , Gothenburg, Sweden

Outline

1. Introduction 2. Proposed Methods 3. Experimental Results 4. Summary

1

Introduction

Introduction

Problem Statement and Motivation

1. How to predict lateral control signals from camera images? • Modular system approach • Explicit control of data flow through sub-modules

• Holistic system approach • Intermediate levels of processing abstracted away • Don’t require explicit feature engineering • Don’t require object/semantics annotated data for learning

2. How to evaluate control signals and performance in a good way? • Vehicle tests • Realistic

• Closed loop simulation • Fast, inexpensive, safe

2

Introduction

Background and Contributions

• End to end learning for self-driving cars [Bojarski et al., 2016b] • PilotNet, 9 layer CNN with ∼250k trainable parameters • Images captured from 3 front cameras for training • Lane following with 98% level of autonomy

• Our contributions • Experimentally show that data augmentation might not be necessary for learning LKA functionality (using a model based on PilotNet) • Propose 2 metrics for numeric evaluation based on safety (positioning) and comfort (trajectory smoothness)

3

Proposed Methods

Proposed Methods

Approach

• Model a driving policy πθ as a convolutional neural network • Find θ∗ = argminθ E(s,a)∼dπ∗ [l(a, πθ (s))] by supervised learning • Learn a mapping from states to actions using state–action pairs sampled from regular highway driving, without data augmentation Expert action: a

State: s

Policy: πθ

Action: πθ (s)

− Loss: l(a, πθ (s))

Policy adjustment

4

Proposed Methods

Data

• 640×480 images from Volvo Cars test expeditions • Dataset selection of approximately 2.5M images sampled at 20Hz

5

Proposed Methods

Actions

• Actions (and states) pruned to be more ”uniformly” distributed • Resulting dataset of approximately 1.4M state–action pairs (27h) • Steering wheel angle → curvature

−1

−0.5

0

0.5

SWA [rad]

1

−1

−0.5

0

0.5

1

SWA [rad] 6

Proposed Methods

CNN Architecture

• Based on PilotNet [Bojarski et al., 2016b] • 264k trainable parameters • Trained for 9 epochs of the dataset • Performance?

1

10

FC4

50

FC3

100

FC2

1216

FC1

FLAT.

C5

1 × 16 × 76

C4

3 × 18 × 64

C3

5 × 20 × 48

C2

14 × 43 × 36

C1

32 × 89 × 24

68 × 182 × 1

IMAGE

PRE.

1 r

7

Proposed Methods

Positioning Penalty

• Penalty width w and shape factor β road and situation dependent

ep (d; w , β) =

   1

d <0 d w

(βw ) − βd   0

0≤d ≤w d >w

Positioning penalty

wL

wl

wL0

wr

β=1 β = 0.1 β = 0.01

1 0.5 0 0

w /2

w

Distance to lane marking: d

vehicle

dl

dr wv

8

Proposed Methods

Discomfort Penalty

• Based on penalty from a vehicle motion model [S¨ orstedt et al., 2011] 2 3 • Comfort level g ≈ 1.8 m/s (or 1.8 m/s ) [Felipe and Navin, 1998, Xu et al., 2015]  2  y2 g ed (y ; g ) =   5+ 6

if x < g 2

y 6g 2

6

if x ≥ g

Level of discomfort

10 Comfortable Uncomfortable

5

0 0

1 2 Lateral acceleration

m s2

3 or jerk sm3

vehicle

9

Experimental Results

Experimental Results

Reality Gap

Example: Visual backpropagation [Bojarski et al., 2016a] • Pixel regions that contribute most to control decision/prediction

Volvo (real)

CarMaker (synthetic)

Unity (synthetic)

10

Experimental Results

Discomfort

s2

acceleration

m

Example: Lateral acceleration and jerk

0 −2

πθ π∗

2,900

3,000

3,100

3,200

3,300

s3

jerk

m

4

3,400 πθ π∗

2 0 −2 2,900

3,000

3,100

3,200

3,300

3,400

Road distance [m] 11

Experimental Results

Performance

Example: 34km road geometry, β = 0.01, wl = wr = 0.4m, g = 1.8 • Positioning: badly positioned only ∼ 2% of the time • Acceleration: ∼ 9% more uncomfortable, but still comfortable • Jerk: ∼ 279% more uncomfortable, but still comfortable πθ

π∗

πθ /π ∗

0.006 0.013

0 0

– –

Avg ed (yacc ; g ) Max ed (yacc ; g )

0.152 13.283

0.140 11.890

1.086 1.117

Avg ed (yjerk ; g ) Max ed (yjerk ; g )

0.064 19.816

0.023 11.230

2.783 1.765

Penalty Metric Avg ep (dl ; wl , β) Avg ep (dr ; wr , β)

12

Summary

Summary

Conclusions

• The learnt policy seems to provide robust behaviour in simulated environments without data augmentation. • Instantaneous decisions provides noisy behaviour, filtering based on previous decisions improves the driving behaviour. • More research on safety and verification aspects by understanding the internal representations of networks needed. More videos at: http://goo.gl/MKKnuF

13

References i

Bojarski, M., Choromanska, A., Choromanski, K., Firner, B., Jackel, L., Muller, U., and Zieba, K. (2016a). Visualbackprop: visualizing cnns for autonomous driving. arXiv preprint arXiv:1611.05418. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016b). End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316. Felipe, E. and Navin, F. (1998). Canadian researchers test driver response to horizontal curves. Road Management & Engineering Journal TranSafety, Inc, 1.

References ii

S¨ orstedt, J., Svensson, L., Sandblom, F., and Hammarstrand, L. (2011). A new vehicle motion model for improved predictions and situation assessment. IEEE Transactions on Intelligent Transportation Systems, 12(4):1209–1219. Xu, J., Yang, K., Shao, Y., and Lu, G. (2015). An experimental study on lateral acceleration of cars in different environments in sichuan, southwest China. Discrete Dynamics in Nature and Society, 2015.