End-to-End Deep Learning for Autonomous Longitudinal and Lateral Control based on Vehicle Dynamics

Hsu, Tsung-Ming; Wang, Cheng-Hsien; Chen, Yu-Rui

doi:10.1145/3293663.3293677

Cited by 15 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tsung-Ming Hsu et al presented a deep learning model to mimic driving behaviors by learning the dynamic information of the vehicle along with image information in order to improve the performance of a self-driving vehicle. For the implementation of the model, they placed traffic cones on the road to collect the scene of avoiding obstacles [7].…”

Section: Related Workmentioning

confidence: 99%

TraCon: A novel dataset for real-time traffic cones detection using deep learning

Katsamenis¹,

Karolou²,

Davradou³

et al. 2022

Preprint

View full text Add to dashboard Cite

Substantial progress has been made in the field of object detection in road scenes. However, it is mainly focused on vehicles and pedestrians. To this end, we investigate traffic cone detection, an object category crucial for road effects and maintenance. In this work, the YOLOv5 algorithm is employed, in order to find a solution for the efficient and fast detection of traffic cones. The YOLOv5 can achieve a high detection accuracy with the score of IoU up to 91.31%. The proposed method is been applied to an RGB roadwork image dataset, collected from various sources.

show abstract

Section: Related Workmentioning

confidence: 99%

TraCon: A novel dataset for real-time traffic cones detection using deep learning

Katsamenis¹,

Karolou²,

Davradou³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Vehicle trajectory tracking control is also a critical part of autonomous driving for tracking results from motion planning module. There are many control algorithms for vehicle control, such as controllers based on proportion integration differentiation (PID) and pure pursuit [25], MPC [5] [22], learningbased MPC [26], linear quadratic regulator (LQR) [27], and E2E control approaches including supervised learning and deep reinforcement learning (DRL) [28] [29]. PID is the most commonly used in practice because it is model-free and stable.…”

Section: Introductionmentioning

confidence: 99%

DDK: A Deep Koopman Approach for Dynamics Modeling and Trajectory Tracking of Autonomous Vehicles

Xiao¹

2021

Preprint

View full text Add to dashboard Cite

Autonomous driving has attracted lots of attention in recent years. An accurate vehicle dynamics is important for autonomous driving techniques, e.g. trajectory prediction, motion planning, and control of trajectory tracking. Although previous works have made some results, the strong nonlinearity, precision, and interpretability of dynamics for autonomous vehicles are open problems worth being studied. In this paper, the approach based on the Koopman operator named deep direct Koopman (DDK) is proposed to identify the model of the autonomous vehicle and the identified model is a linear time-invariant (LTI) version, which is convenient for motion planning and controller design. In the approach, the Koopman eigenvalues and system matrix are considered as trainable tensors with the original states of the autonomous vehicle being concatenated to a part of the Koopman eigenfunctions so that a physically interpretable subsystem can be extracted from the identified latent dynamics. Subsequently, the process of the identification model is trained under the proposed method based on the dataset which consists of about 60km of data collected with a real electric SUV while the effectiveness of the identified model is validated. Meanwhile, a high-fidelity vehicle dynamics is identified in CarSim with DDK, and then, a linear model predictive control (MPC) called DDK-MPC integrating DDK is designed to validate the performance for the control of trajectory tracking. Simulation results illustrate that the model of the nonlinear vehicle dynamics can be identified effectively via the proposed method and that excellent tracking performance can be obtained with the identified model under DDK-MPC.

show abstract

“…Artificial neural networks have transformed the field of computer vision and are increasingly used in robotics to support, e.g. perception [1]- [4], simultaneous localisation and mapping [5]- [8], planning [9], [10], and end-to-end control [11]- [13]. To achieve good performance, large neural networks are required that need to be deployed on specialpurpose hardware to meet the latency and power-consumption constraints of autonomous robots.…”

Section: Introductionmentioning

confidence: 99%

Towards neuromorphic control: A spiking neural network based PID controller for UAV

Stagsted¹,

Vitale

Binz³

et al. 2020

Robotics: Science and Systems XVI

View full text Add to dashboard Cite

In this work, we present a spiking neural network (SNN) based PID controller on a neuromorphic chip. On-chip SNNs are currently being explored in low-power AI applications. Due to potentially ultra-low power consumption, low latency, and high processing speed, on-chip SNNs are a promising tool for control of power-constrained platforms, such as Unmanned Aerial Vehicles (UAV). To obtain highly efficient and fast end-toend neuromorphic controllers, the SNN-based AI architectures must be seamlessly integrated with motor control. Towards this goal, we present here the first implementation of a fully neuromorphic PID controller. We interfaced Intel's neuromorphic research chip Loihi to a UAV, constrained to a single degree of freedom. We developed an SNN control architecture using populations of spiking neurons, in which each spike carries information about the measured, control, or error value, defined by the identity of the spiking neuron. Using this sparse code, we realize a precise PID controller. The P, I, and D gains of the controller are implemented as synaptic weights that can adapt according to an on-chip plasticity rule. In future work, these plastic synapses can be used to tune and adapt the controller autonomously.

show abstract

End-to-End Deep Learning for Autonomous Longitudinal and Lateral Control based on Vehicle Dynamics

Cited by 15 publications

References 3 publications

TraCon: A novel dataset for real-time traffic cones detection using deep learning

TraCon: A novel dataset for real-time traffic cones detection using deep learning

DDK: A Deep Koopman Approach for Dynamics Modeling and Trajectory Tracking of Autonomous Vehicles

Towards neuromorphic control: A spiking neural network based PID controller for UAV

Contact Info

Product

Resources

About