Abstract

Recent deep learning techniques promise high hopes for self-driving cars while there are still many issues to be addressed such as uncertainties (e.g., extreme weather conditions) in learned models. In this work, for the uncertainty-aware lane keeping, we first propose a convolutional mixture density network (CMDN) model that estimates the lateral position error, the yaw angle error, and their corresponding uncertainties from the camera vision. We then establish a vision-based uncertainty-aware lane keeping strategy in which a high-level reinforcement learning policy hierarchically modulates the reference longitudinal speed as well as the low-level lateral control. Finally, we evaluate the robustness of our strategy against the uncertainties of the learned CMDN model coming from unseen or noisy situations, as compared to the conventional lane keeping strategy without taking into account such uncertainties. Our uncertainty-aware strategy outperformed the conventional lane keeping strategy, without a lane departure in our test scenario during high-uncertainty periods with random occurrences of fog and rain situations on the road. The successfully trained deep reinforcement learning agent slows down the vehicle speed and tries to minimize the lateral error during high uncertainty situations similarly to what human drivers would do in such situations.

References

1.
Deng
,
L.
,
2014
, “
A Tutorial Survey of Architectures, Algorithms, and Applications for Deep Learning
,”
APSIPA Trans. Signal Inf. Process.
,
3
, pp. 1–29.10.1017/atsip.2013.9
2.
Yadron
,
D.
, and
Tynan
,
D.
,
2016
, “
Tesla Driver Dies in First Fatal Crash While Using Autopilot Mode
,”
The Guardian
,
1
.
3.
Kendall
,
A.
, and
Gal
,
Y.
,
2017
, “
What Uncertainties Do we Need in Bayesian Deep Learning for Computer Vision?
,”
Advances in Neural Information Processing Systems
, Long Beach, CA, Oct. 5, pp.
5574
5584
.
4.
Gal
,
Y.
,
2016
, “
Uncertainty in Deep Learning
,” Ph.D. thesis,
University of Cambridge
,
Cambridge, UK
.
5.
Gal
,
Y.
, and
Ghahramani
,
Z.
,
2016
, “
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
,”
International Conference on Machine Learning
, New York, June 20–22, pp.
1050
1059
.
6.
Choi
,
S.
,
Lee
,
K.
,
Lim
,
S.
, and
Oh
,
S.
,
2018
, “
Uncertainty-Aware Learning From Demonstration Using Mixture Density Networks With Sampling-Free Variance Modeling
,” IEEE International Conference on Robotics and Automation (
ICRA
),
Brisbane
,
Australia
, May 21–26, pp.
6915
6922
.10.1109/ICRA.2018.8462978
7.
Xu
,
Y.
,
Choi
,
J.
, and
Oh
,
S.
,
2011
, “
Mobile Sensor Network Navigation Using Gaussian Processes With Truncated Observations
,”
IEEE Trans. Rob.
,
27
(
6
), pp.
1118
1131
.10.1109/TRO.2011.2162766
8.
Stellet
,
J. E.
,
Zofka
,
M. R.
,
Schumacher
,
J.
,
Schamm
,
T.
,
Niewels
,
F.
, and
Zöllner
,
J. M.
,
2015
, “
Testing of Advanced Driver Assistance Towards Automated Driving: A Survey and Taxonomy on Existing Approaches and Open Questions
,”
IEEE 18th International Conference on Intelligent Transportation Systems
,
Las Palmas de Gran Canaria
,
Spain
, Sept. 15–18, pp.
1455
1462
.
9.
Enache
,
N. M.
,
Mammar
,
S.
,
Lusetti
,
B.
, and
Sebsadji
,
Y.
,
2011
, “
Active Steering Assistance for Lane Keeping and Lane Departure Prevention
,”
ASME J. Dyn. Syst., Meas., Control
,
133
(
6
), p. 061003.10.1115/1.4003801
10.
Li
,
R.
,
Wang
,
W.
,
Chen
,
Y.
,
Srinivasan
,
S.
, and
Krovi
,
V. N.
,
2018
, “
An End-to-End Fully Automatic Bay Parking Approach for Autonomous Vehicles
,”
ASME
Paper No. DSCC2018-9126.10.1115/DSCC2018-9126
11.
Kim
,
M.
,
Lee
,
S.
,
Lim
,
J.
,
Choi
,
J.
, and
Kang
,
S. G.
,
2020
, “
Unexpected Collision Avoidance Driving Strategy Using Deep Reinforcement Learning
,”
IEEE Access
,
8
, pp.
17243
17252
.10.1109/ACCESS.2020.2967509
12.
Liu
,
J.-F.
,
Wu
,
J.-H.
, and
Su
,
Y.-F.
,
2007
, “
Development of an Interactive Lane Keeping Control System for Vehicle
,”
IEEE Vehicle Power and Propulsion Conference
,
Arlington
,
TX
, Sept. 9–12, pp.
702
706
.
13.
Park
,
J.
,
Son
,
H.
,
Lee
,
J.
, and
Choi
,
J.
,
2019
, “
Driving Assistant Companion With Voice Interface Using Long Short-Term Memory Networks
,”
IEEE Trans. Ind. Inf.
,
15
(
1
), pp.
582
590
.10.1109/TII.2018.2861739
14.
Shah
,
S.
,
Dey
,
D.
,
Lovett
,
C.
, and
Kapoor
,
A.
,
2018
, “
Airsim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
,”
In Field and Service Robotics
,
Springer
,
Cham, Switzerland
, pp.
621
635
.
15.
Rajamani
,
R.
,
2011
,
Vehicle Dynamics and Control
,
Springer Science & Business Media
,
New York
.
16.
Bishop
,
C. M.
,
1994
, “
Mixture Density Networks
,” Technical Report,
Aston University
,
Birmingham, UK
.
17.
Schulman
,
J.
,
Wolski
,
F.
,
Dhariwal
,
P.
,
Radford
,
A.
, and
Klimov
,
O.
,
2017
, “
Proximal Policy Optimization Algorithms
,” arXiv preprint arXiv:1707.06347.
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