scholarly journals System, Design and Experimental Validation of Autonomous Vehicle in an Unconstrained Environment

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 5999
Author(s):  
Shoaib Azam ◽  
Farzeen Munir ◽  
Ahmad Muqeem Sheri ◽  
Joonmo Kim ◽  
Moongu Jeon
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Experimental Validation ◽  
Autonomous Vehicle ◽  
Cost Effective ◽  
Longitudinal Control ◽  
Sensor Layer ◽  
Traffic Efficiency ◽  
Control Layer

In recent years, technological advancements have made a promising impact on the development of autonomous vehicles. The evolution of electric vehicles, development of state-of-the-art sensors, and advances in artificial intelligence have provided necessary tools for the academia and industry to develop the prototypes of autonomous vehicles that enhance the road safety and traffic efficiency. The increase in the deployment of sensors for the autonomous vehicle, make it less cost-effective to be utilized by the consumer. This work focuses on the development of full-stack autonomous vehicle using the limited amount of sensors suite. The architecture aspect of the autonomous vehicle is categorized into four layers that include sensor layer, perception layer, planning layer and control layer. In the sensor layer, the integration of exteroceptive and proprioceptive sensors on the autonomous vehicle are presented. The perception of the environment in term localization and detection using exteroceptive sensors are included in the perception layer. In the planning layer, algorithms for mission and motion planning are illustrated by incorporating the route information, velocity replanning and obstacle avoidance. The control layer constitutes lateral and longitudinal control for the autonomous vehicle. For the verification of the proposed system, the autonomous vehicle is tested in an unconstrained environment. The experimentation results show the efficacy of each module, including localization, object detection, mission and motion planning, obstacle avoidance, velocity replanning, lateral and longitudinal control. Further, in order to demonstrate the experimental validation and the application aspect of the autonomous vehicle, the proposed system is tested as an autonomous taxi service.

scholarly journals An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking

2021 ◽  
Vol 10 (3) ◽  
pp. 687
Author(s):  
Naitik Nakrani ◽  
Maulin M. Joshi
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Vehicle Dynamics ◽  
Autonomous Vehicles ◽  
Intelligent Design ◽  
Autonomous Vehicle ◽  
Planning Techniques ◽  
Vehicle Localization ◽  
Adaptive Motion ◽  
Planning Algorithm

In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

A novel path tracking approach considering safety of the intended functionality for autonomous vehicles

2021 ◽  
pp. 095440702110205
Author(s):  
Huiran Wang ◽  
Qidong Wang ◽  
Wuwei Chen ◽  
Linfeng Zhao ◽  
Dongkui Tan
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Coordinated Control ◽  
Steering System ◽  
Front Wheel ◽  
Path Tracking ◽  
Hierarchical Architecture ◽  
Automatic Steering ◽  
The Stability ◽  
Control Layer

To reduce the adverse effect of the functional insufficiency of the steering system on the accuracy of path tracking, a path tracking approach considering safety of the intended functionality is proposed by coordinating automatic steering and differential braking in this paper. The proposed method adopts a hierarchical architecture consisting of a coordinated control layer and an execution control layer. In coordinated control layer, an extension controller considering functional insufficiency of the steering system, tire force characteristics and vehicle driving stability is proposed to determine the weight coefficients of automatic steering and the differential braking, and a model predictive controller is designed to calculate the desired front wheel angle and additional yaw moment. In execution control layer, a H∞ steering angle controller considering external disturbances and parameter uncertainty is designed to track desired front wheel angle, and a braking force distribution module is used to determine the wheel cylinder pressure of the controlled wheels. Both simulation and experiment results show that the proposed method can overcome the functional insufficiency of the steering system and improve the accuracy of path tracking while maintaining the stability of the autonomous vehicle.

scholarly journals Deep reinforcement learning based control for Autonomous Vehicles in CARLA

2022 ◽  
Author(s):  
Óscar Pérez-Gil ◽  
Rafael Barea ◽  
Elena López-Guillén ◽  
Luis M. Bergasa ◽  
Carlos Gómez-Huélamo ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Data Sources ◽  
Simulation Environment ◽  
Urban Simulation ◽  
Policy Gradient ◽  
Almost All ◽  
Control Layer

AbstractNowadays, Artificial Intelligence (AI) is growing by leaps and bounds in almost all fields of technology, and Autonomous Vehicles (AV) research is one more of them. This paper proposes the using of algorithms based on Deep Learning (DL) in the control layer of an autonomous vehicle. More specifically, Deep Reinforcement Learning (DRL) algorithms such as Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) are implemented in order to compare results between them. The aim of this work is to obtain a trained model, applying a DRL algorithm, able of sending control commands to the vehicle to navigate properly and efficiently following a determined route. In addition, for each of the algorithms, several agents are presented as a solution, so that each of these agents uses different data sources to achieve the vehicle control commands. For this purpose, an open-source simulator such as CARLA is used, providing to the system with the ability to perform a multitude of tests without any risk into an hyper-realistic urban simulation environment, something that is unthinkable in the real world. The results obtained show that both DQN and DDPG reach the goal, but DDPG obtains a better performance. DDPG perfoms trajectories very similar to classic controller as LQR. In both cases RMSE is lower than 0.1m following trajectories with a range 180-700m. To conclude, some conclusions and future works are commented.

Emergency obstacle avoidance trajectory tracking control based on active disturbance rejection for autonomous vehicles

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092110
Author(s):  
Runqiao Liu ◽  
Minxiang Wei ◽  
Nan Sang
Keyword(s):  
Obstacle Avoidance ◽  
Disturbance Rejection ◽  
Autonomous Vehicles ◽  
High Speed ◽  
Autonomous Vehicle ◽  
Angular Frequency ◽  
Vehicle Speed ◽  
Feed Forward ◽  
Yaw Rate ◽  
Active Disturbance Rejection

To solve the problem of understeer and oversteer for autonomous vehicle under high-speed emergency obstacle avoidance conditions, considering the effect of steering angular frequency and vehicle speed on yaw rate for four-wheel steering vehicles in the frequency domain, a feed-forward controller for four-wheel steering autonomous vehicles that tracks the desired yaw rate is proposed. Furthermore, the steering sensitivity coefficient of the vehicle is compensated linearly with the change in the steering angular frequency and vehicle speed. In addition, to minimize the tracking errors caused by vehicle nonlinearity and external disturbances, an active disturbance rejection control feedback controller that tracks the desired lateral displacement and desired yaw angle is designed. Finally, CarSim® obstacle avoidance simulation results show that an autonomous vehicle with the four-wheel steering path tracking controller consisting of feed-forward control and feedback control could not only improve the tire lateral forces but also reduce tail flicking (oversteer) and pushing ahead (understeer) under high-speed emergency obstacle avoidance conditions.

scholarly journals Motion Planning for Autonomous Vehicles Considering Longitudinal and Lateral Dynamics Coupling

2020 ◽  
Vol 10 (9) ◽  
pp. 3180 ◽  
Author(s):  
Dongfang Dang ◽  
Feng Gao ◽  
Qiuxia Hu
Keyword(s):  
Motion Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Dynamical Model ◽  
Planning Problem ◽  
Original Design ◽  
Automatic Driving ◽  
Wide Range ◽  
Planning Algorithm ◽  
Dynamical Coupling

Vehicles are highly coupled and multi-degree nonlinear systems. The establishment of an appropriate vehicle dynamical model is the basis of motion planning for autonomous vehicles. With the development of autonomous vehicles from L2 to L3 and beyond, the automatic driving system is required to make decisions and plans in a wide range of speeds and on bends with large curvature. In order to make precise and high-quality control maneuvers, it is important to account for the effects of dynamical coupling in these working conditions. In this paper, a new single-coupled dynamical model (SDM) is proposed to deal with the various dynamical coupling effects by identifying and simplifying the complicated one. An autonomous vehicle motion planning problem is then formulated using the nonlinear model predictive control theory (NMPC) with the SDM constraint (NMPC-SDM). We validated the NMPC-SDM with hardware-in-the-loop (HIL) experiments to evaluate improvements to control performance by comparing with the planners original design, using the kinematic and single-track models. The comparative results show the superiority of the proposed motion planning algorithm in improving the maneuverability and tracking performance.

scholarly journals A Cost-Effective Photonic Radar Under Adverse Weather Conditions for Autonomous Vehicles by Incorporating a Frequency-Modulated Direct Detection Scheme

2021 ◽  
Vol 9 ◽  
Author(s):  
Abhishek Sharma ◽  
Sushank Chaudhary ◽  
Jyoteesh Malhotra ◽  
Muhammad Saadi ◽  
Sattam Al Otaibi ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Direct Detection ◽  
Signal To Noise Ratio ◽  
Autonomous Vehicle ◽  
Cost Effective ◽  
Weather Conditions ◽  
Atmospheric Conditions ◽  
Detection Scheme ◽  
Available Bandwidth ◽  
Adverse Weather

In recent years, there have been plenty of demands and growth in the autonomous vehicle industry, and thus, challenges of designing highly efficient photonic radars that can detect and range any target with the resolution of a few centimeters have been encountered. The existing radar technology is unable to meet such requirements due to limitations on available bandwidth. Another issue is to consider strong attenuation while working under diverse atmospheric conditions at higher frequencies. The proposed model of photonic radar is developed considering these requirements and challenges using the frequency-modulated direct detection technique and considering a free-space range of 750 m. The result depicts improved range detection in terms of received power and an acceptable signal-to-noise ratio and range under adverse climatic situations.

MPC-Based Control of Autonomous Vehicles With Localized Path Planning for Obstacle Avoidance Under Uncertainties

2019 ◽  
Author(s):  
Sai Rajeev Devaragudi ◽  
Bo Chen
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Sensor Data ◽  
Constant Time ◽  
Longitudinal Motion ◽  
Driver Assistance Systems

Abstract This paper presents a Model Predictive Control (MPC) approach for longitudinal and lateral control of autonomous vehicles with a real-time local path planning algorithm. A heuristic graph search method (A* algorithm) combined with piecewise Bezier curve generation is implemented for obstacle avoidance in autonomous driving applications. Constant time headway control is implemented for a longitudinal motion to track lead vehicles and maintain a constant time gap. MPC is used to control the steering angle and the tractive force of the autonomous vehicle. Furthermore, a new method of developing Advanced Driver Assistance Systems (ADAS) algorithms and vehicle controllers using Model-In-the-Loop (MIL) testing is explored with the use of PreScan®. With PreScan®, various traffic scenarios are modeled and the sensor data are simulated by using physics-based sensor models, which are fed to the controller for data processing and motion planning. Obstacle detection and collision avoidance are demonstrated using the presented MPC controller.

scholarly journals Safety Evaluation for Connected and Autonomous Vehicles’ Exclusive Lanes considering Penetrate Ratios and Impact of Trucks Using Surrogate Safety Measures

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jian Zhang ◽  
Kunrun Wu ◽  
Min Cheng ◽  
Min Yang ◽  
Yang Cheng ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Safety Evaluation ◽  
Driver Model ◽  
Microscopic Simulation ◽  
Safety Measures ◽  
Traffic Efficiency ◽  
Safety Improvement ◽  
Demand Scenario ◽  
Surrogate Safety Measures

Plenty of studies on exclusive lanes for Connected and Autonomous Vehicle (CAV) have been conducted recently about traffic efficiency and safety. However, most of the previous research studies neglected comprehensive consideration of the safety impact on different market penetration rates (MPRs) of CAVs, traffic demands, and proportion of trucks in mixture CAVs with human’s driven vehicle environment. On this basis, this study is to (1) identify the safety impact on exclusive lanes for CAVs under different MPRs with different traffic demands and (2) investigate the safety impact of trucks for CAV exclusive lanes on mixture environment. Based on the Intelligent Driver Model (IDM), a CAV platooning control algorithm is proposed for modeling the driving behaviors of CAVs. A calibrated 7-kilometer freeway section microscopic simulation environment is built by VISSIM. Four surrogate safety measures, including both longitudinal and lateral safety risk indexes, are employed to evaluate the overall safety impacts of setting exclusive lanes. Main results indicate that (1) setting one exclusive lane is capable to improve overall safety environment in low demand, and two exclusive lanes are more suitable for high-demand scenario; (2) existence of trucks worsens overall longitudinal safety environment, and improper setting of exclusive lanes in high trucks, low MPR scenario has adverse effect on longitudinal safety; and (3) setting exclusive lanes have better longitudinal and lateral safety improvement in high-truck proportion scenarios. Setting one or two exclusive lanes led to [+42.4% to −52.90%] and [+45.7% to −55.2%] of longitudinal risks while [−1.8% to −87.1%] and [−2.1% to −85.3%] of lateral conflicts compared with the base scenario, respectively. Results of this study provide useful insight for the setting of exclusive lanes for CAVs in a mixture environment.

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