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The majority of the past research dealing with lane-changing controller design of autonomous vehicles (𝐴𝑉 s) is based on the assumption of full knowledge of the model dynamics of the 𝐴𝑉 and the surrounding vehicles. However, in the real world, this is not a very realistic assumption as accurate dynamic models are difficult to obtain. Also, the dynamic model parameters might change over time due to various factors. Thus, there is a need for a learning-based lane change controller design methodology that can learn the optimal control policy in real time using sensor data. In this paper, we have addressed this need by introducing an optimal learningbased control methodology that can solve the real-time lane-changing problem of 𝐴𝑉 s, where the input-state data of the 𝐴𝑉 is utilized to generate a near-optimal lane-changing controller by approximate/adaptive dynamic programming (ADP) technique. In the case of this type of complex lane-changing maneuver, the lateral dynamics depend on the longitudinal velocity of the vehicle. If the longitudinal velocity is assumed constant, a linear parameter invariant model can be used. However, assuming constant velocity while performing a lane-changing maneuver is not a realistic assumption. This assumption might increase the risk of accidents, especially in the case of lane abortion when the surrounding vehicles are not cooperative. Thus, in this paper, the dynamics of the 𝐴𝑉 are assumed to be a linear parameter-varying system. Thus we have two challenges for the lane-changing controller design: parameter-varying, and unknown dynamics. With the help of both gain scheduling and ADP techniques combined, a learning-based control algorithm that can generate a near-optimal lane-changing controller without having to know the accurate dynamic model of the 𝐴𝑉 is proposed. The inclusion of a gain scheduling approach with ADP makes the controller applicable to non-linear and/or parameter-varying 𝐴𝑉 dynamics. The stability of the learning-based gain scheduling controller has also been rigorously proved. Moreover, a data-driven lane-changing decision-making algorithm is introduced that can make the 𝐴𝑉 perform a lane abortion if safety conditions are violated during a lane change. Finally, the proposed learning-based gain scheduling controller design algorithm and the lane-changing decision-making methodology are numerically validated using MATLAB, SUMO simulations, and the NGSIM dataset.
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Deep Learning Based Anomaly Detection for Lane Changing Decision
Vehicles can utilize their sensors or receive messages from other vehicles to acquire information about the surrounding environments. However, the information may be inaccurate, faulty, or maliciously compromised due to sensor failures, communication faults, or security attacks. The goal of this work is to detect if a lane-changing decision and the sensed or received information are anomalous. We develop three anomaly detection approaches based on deep learning: a classifier approach, a predictor approach, and a hybrid approach combining the classifier and the predictor. All of them do not need anomalous data nor lateral features so that they can generally consider lane-changing decisions before the vehicles start moving along the lateral axis. They achieve at least 82% and up to 93% F1 scores against anomaly on data from Simulation of Urban MObility (SUMO) and HighD. We also examine system properties and verify that the detected anomaly includes more dangerous scenarios.
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- Award ID(s):
- 1908549
- PAR ID:
- 10374121
- Date Published:
- Journal Name:
- IEEE Intelligent Vehicles Symposium (IV 2022)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IV51971.2022.9827293
