Control Strategies for Autonomous Vehicles

Samak, Chinmay Vilas; Samak, Tanmay Vilas; Kandhasamy, Sivanathan

doi:10.1201/9781003048381-3

Cited by 20 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the lane-changing operation (LC), adhering to the guidelines outlined in the "Urban Expressway Design Code of the People's Republic of China (CJJ129-2009)", a lane width of W = 3.75 m was utilized, with a designated completion time of 3 s for the lane-change maneuver. Two validated classical trajectory tracking longitudinal and lateral control methods were selected for comparison as follows: one method combines PID longitudinal velocity control with model predictive control (MPC) for lateral motion control (PM) [5,6], while the other combines PID longitudinal velocity control with pure tracking lateral motion control (PP) [7][8][9].…”

Section: Validation Of Control Policy Effectiveness and Generalizationmentioning

confidence: 99%

“…In the current academic discourse on trajectory tracking for intelligent vehicles, the mainstream approach involves separately designing lateral motion controllers and longitudinal motion controllers, which are then combined. However, these designed control laws often demand high precision in vehicle dynamic parameters and computational resources [4][5][6][7][8][9]. Common methods for designing longitudinal controllers include proportional-integral-derivative (PID) methods, sliding mode control algorithms, or model predictive control (MPC) methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Drive Autonomous Vehicle Trajectory Tracking Control Based on Multi-Agent Deep Reinforcement Learning

Liu,

Ding,

Yang

et al. 2024

Mathematics

View full text Add to dashboard Cite

In order to enhance the trajectory tracking accuracy of distributed-driven intelligent vehicles, this paper formulates the tasks of torque output control for longitudinal dynamics and steering angle output control for lateral dynamics as Markov decision processes. To dissect the requirements of action output continuity for longitudinal and lateral control, this paper adopts the deep deterministic policy gradient algorithm (DDPG) for longitudinal velocity control and the deep Q-network algorithm (DQN) for lateral motion control. Multi-agent reinforcement learning methods are applied to the task of trajectory tracking in distributed-driven vehicle autonomous driving. By contrasting with two classical trajectory tracking control methods, the proposed approach in this paper is validated to exhibit superior trajectory tracking performance, ensuring that both longitudinal velocity deviation and lateral position deviation of the vehicle remain at lower levels. Compared with classical control methods, the maximum lateral position deviation is improved by up to 90.5% and the maximum longitudinal velocity deviation is improved by up to 97%. Furthermore, it demonstrates excellent generalization and high computational efficiency, and the running time can be reduced by up to 93.7%.

show abstract

Section: Validation Of Control Policy Effectiveness and Generalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Drive Autonomous Vehicle Trajectory Tracking Control Based on Multi-Agent Deep Reinforcement Learning

Liu,

Ding,

Yang

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…Due to the abundance of data, machine learning models may now be trained to carry out complex tasks such as driving. As a result, instead of using classical methods for controlling a vehicle, such as Proportional Integral Derivative (PID) controllers or Model Predictive Control (MPC) [3], some alternative machine learning methods could be used.…”

Section: Introductionmentioning

confidence: 99%

Implementing Deep Reinforcement Learning in Autonomous Control Systems

Noureldin Ragheb,

Mervat M. A. Mahmoud

2024

ARASET

View full text Add to dashboard Cite

Developing a safe and reliable autonomous vehicle has been a significant focus in recent years. Supervised learning methods require large amounts of labelled data for training, making it expensive. The performance of these agents is limited to the data provided in training and the inability to generalize performance in different environments. In addition, some driving situations, such as near-accident scenarios, are difficult to cover in the training data. As a result, the autonomous driving agent may behave unexpectedly in safety-critical situations, making it unreliable for safe transportation. Reinforcement learning is a potential solution for these issues. This research paper explores the potential of applying deep reinforcement learning techniques to autonomous driving, with a spotlight on comparing two popular deep reinforcement learning algorithms: Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG). The study uses the CARLA simulator, which provides a realistic environment and conditions for testing autonomous driving algorithms. The study finds that DDPG outperforms DQN regarding average reward, but DQN performs better regarding collision rate.

show abstract

“…Neuromorphic brain-inspired control systems, which are based on densely connected spiking neural networks (SNNs) (Tsur, 2021 ), offer a promising alternative with greater energy efficiency and comparable accuracy and latency (DeWolf et al, 2020 ), DeWolf ( 2021 ) to system control. In this work, we propose a neuromorphic implementation of four well-established path-tracking control models for autonomous driving (Samak et al, 2021 ), within a physics-aware computational framework. Our proposed ADSs utilize a LiDAR sensor to estimate the vehicle's position along the track.…”

Section: Introductionmentioning

confidence: 99%

“…Model predictive control (MPC), which employs a predictive model to evaluate the system's future state and optimizes the control policy accordingly. In contrast to conventional artificial neural networks-based controllers, which optimize long-term policies but may pose unexplained, unsafe, and harmful consequences in the short term (Alcala et al, 2018 ), we chose these control models as they are widely utilized in reliable strategies (Samak et al, 2021 ) and offer comfortable, safe, explainable, and interpretable motion control (Berkenkamp et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Autonomous driving controllers with neuromorphic spiking neural networks

Halaly

Tsur

2023

Front. Neurorobot.

View full text Add to dashboard Cite

Autonomous driving is one of the hallmarks of artificial intelligence. Neuromorphic (brain-inspired) control is posed to significantly contribute to autonomous behavior by leveraging spiking neural networks-based energy-efficient computational frameworks. In this work, we have explored neuromorphic implementations of four prominent controllers for autonomous driving: pure-pursuit, Stanley, PID, and MPC, using a physics-aware simulation framework. We extensively evaluated these models with various intrinsic parameters and compared their performance with conventional CPU-based implementations. While being neural approximations, we show that neuromorphic models can perform competitively with their conventional counterparts. We provide guidelines for building neuromorphic architectures for control and describe the importance of their underlying tuning parameters and neuronal resources. Our results show that most models would converge to their optimal performances with merely 100–1,000 neurons. They also highlight the importance of hybrid conventional and neuromorphic designs, as was suggested here with the MPC controller. This study also highlights the limitations of neuromorphic implementations, particularly at higher (> 15 m/s) speeds where they tend to degrade faster than in conventional designs.

show abstract

Control Strategies for Autonomous Vehicles

Cited by 20 publications

References 0 publications

Distributed Drive Autonomous Vehicle Trajectory Tracking Control Based on Multi-Agent Deep Reinforcement Learning

Distributed Drive Autonomous Vehicle Trajectory Tracking Control Based on Multi-Agent Deep Reinforcement Learning

Implementing Deep Reinforcement Learning in Autonomous Control Systems

Autonomous driving controllers with neuromorphic spiking neural networks

Contact Info

Product

Resources

About