Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving

Path planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller integrating obstacle-dependent Gaussian (ODG) and model prediction control (MPC). The ODG algorithm integrates the information from the sensors and calculates the risk factors in the driving environment. The MPC function finds vehicle control signals close to the objective function under limited conditions, such as the structural shape of the vehicle and road driving conditions. The proposed method provides safe control and minimizes vehicle shaking due to the tendency to respond to avoid obstacles quickly. We conducted an experiment using mobile robots, similar to an actual vehicle, to verify the proposed algorithm performance. The experimental results show that the average safety metric is 72.34%, a higher ISO-2631 comport score than others, while the average processing time is approximately 14.2 ms/frame.

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Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment

Sensors ◽

10.3390/s20185053 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5053 ◽

Cited By ~ 1

Author(s):

Saba Arshad ◽

Muhammad Sualeh ◽

Dohyeong Kim ◽

Dinh Van Nam ◽

Gon-Woo Kim

Keyword(s):

Path Planning ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Autonomous Driving ◽

Unified Framework ◽

Human Errors ◽

Safe Driving ◽

Planning And Control ◽

Starting Point ◽

And Control

In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.

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Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

Recent Patents on Computer Science ◽

10.2174/2213275911666181106160002 ◽

2019 ◽

Vol 12 (2) ◽

pp. 120-127 ◽

Cited By ~ 5

Author(s):

Wael Farag

Keyword(s):

Gradient Descent ◽

Autonomous Driving ◽

Driving Behavior ◽

Training Data ◽

Stochastic Gradient Descent ◽

Data Set ◽

Safe Driving ◽

Processing Pipeline ◽

Self Driving Cars ◽

And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

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Occlusion-aware On-road Autonomous Driving: A Path Planning Method Considering Visual Occlusions

Optik ◽

10.1016/j.ijleo.2021.167347 ◽

2021 ◽

pp. 167347

Author(s):

Bai Li

Keyword(s):

Path Planning ◽

Autonomous Driving ◽

Planning Method

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A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

Journal of Marine Science and Engineering ◽

10.3390/jmse9030252 ◽

2021 ◽

Vol 9 (3) ◽

pp. 252

Author(s):

Yushan Sun ◽

Xiaokun Luo ◽

Xiangrui Ran ◽

Guocheng Zhang

Keyword(s):

Path Planning ◽

Obstacle Avoidance ◽

Autonomous Underwater Vehicles ◽

Optimal Path ◽

Small Scale ◽

Target Point ◽

Safe Driving ◽

Policy Gradient ◽

Planning Algorithm ◽

Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

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An extended model of the ISO-2631 standard to objectify the ride comfort in autonomous driving

Work ◽

10.3233/wor-208004 ◽

2021 ◽

Vol 68 (s1) ◽

pp. S37-S45

Author(s):

Georg Burkhard ◽

Tobias Berger ◽

Erik Enders ◽

Dieter Schramm

Keyword(s):

Ride Comfort ◽

Autonomous Driving ◽

Extended Model ◽

Measuring Point ◽

Significant Difference ◽

Comfort Perception ◽

Simulator Study ◽

Iso 2631

BACKGROUND: With the development of autonomous driving, the occupants’ comfort perception and their activities during the drive are becoming increasingly the focus of research. Especially in one of the first applications, a drive on a motorway, vertical dynamics play a major role. OBJECTIVE: To be able to robustly objectify ride comfort, better models need to be developed. Initial studies have shown, that the current ISO-2631 standard creates good results in the objectification and can be regarded as benchmark. METHODS: To increase the accuracy in objectification, an extended model with the occupants’ head as additional measuring point is introduced. Instead of the known frequency filters, weighting (k-factors) is used to differentiate possible excitations. For comparing the model with the ISO-2631, a simulator study with 5 excitations and 50 inattentive subjects is carried out. RESULTS: Evaluating the study with the ISO-2631, 3 out of 5 excitations indicate a significant difference between the occupant’s impression and the calculated comfort value. In comparison the extended model has no significant difference. CONCLUSION: The results further show, that inattentive occupants move their heads significantly more. By measuring accelerations of the head, the extended model creates equivalent or more accurate comfort values than the ISO-2631.

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Lane Level Path Planning for Urban Autonomous Driving using Vector Map

2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) ◽

10.1109/icce-asia49877.2020.9277298 ◽

2020 ◽

Author(s):

Chanyoung Jung ◽

Deagyu Lee ◽

Boseong Kim ◽

David Hyunchul Shim

Keyword(s):

Path Planning ◽

Autonomous Driving ◽

Vector Map

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Online Local Path Planning on the Campus Environment for Autonomous Driving Considering Road Constraints and Multiple Obstacles

Applied Sciences ◽

10.3390/app11093909 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3909

Author(s):

Changhyeon Park ◽

Seok-Cheol Kee

Keyword(s):

Path Planning ◽

Driving Simulator ◽

Autonomous Driving ◽

Campus Environment ◽

Vehicle Localization ◽

Time Operation ◽

National University ◽

Planning Algorithm ◽

Path Planning Algorithm ◽

Multiple Obstacles

In this paper, an urban-based path planning algorithm that considered multiple obstacles and road constraints in a university campus environment with an autonomous micro electric vehicle (micro-EV) is studied. Typical path planning algorithms, such as A*, particle swarm optimization (PSO), and rapidly exploring random tree* (RRT*), take a single arrival point, resulting in a lane departure situation on the high curved roads. Further, these could not consider urban-constraints to set collision-free obstacles. These problems cause dangerous obstacle collisions. Additionally, for drive stability, real-time operation should be guaranteed. Therefore, an urban-based online path planning algorithm, which is robust in terms of a curved-path with multiple obstacles, is proposed. The algorithm is constructed using two methods, A* and an artificial potential field (APF). To validate and evaluate the performance in a campus environment, autonomous driving systems, such as vehicle localization, object recognition, vehicle control, are implemented in the micro-EV. Moreover, to confirm the algorithm stability in the complex campus environment, hazard scenarios that complex obstacles can cause are constructed. These are implemented in the form of a delivery service using an autonomous driving simulator, which mimics the Chungbuk National University (CBNU) campus.

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Real - Time Trajectory and Velocity Planning for Autonomous Vehicles

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e2880.0610521 ◽

2021 ◽

Vol 10 (5) ◽

pp. 439-448

Author(s):

Hrishikesh Dey ◽

Rithika Ranadive ◽

Abhishek Chaudhari

Keyword(s):

Path Planning ◽

Velocity Profile ◽

Real Time ◽

Autonomous Vehicle ◽

Optimal Path ◽

Autonomous Driving ◽

Navigation Algorithm ◽

Velocity Planning ◽

Planning Algorithm ◽

Path Planner

Path planning algorithm integrated with a velocity profile generation-based navigation system is one of the most important aspects of an autonomous driving system. In this paper, a real-time path planning solution to obtain a feasible and collision-free trajectory is proposed for navigating an autonomous car on a virtual highway. This is achieved by designing the navigation algorithm to incorporate a path planner for finding the optimal path, and a velocity planning algorithm for ensuring a safe and comfortable motion along the obtained path. The navigation algorithm was validated on the Unity 3D Highway-Simulated Environment for practical driving while maintaining velocity and acceleration constraints. The autonomous vehicle drives at the maximum specified velocity until interrupted by vehicular traffic, whereas then, the path planner, based on the various constraints provided by the simulator using µWebSockets, decides to either decelerate the vehicle or shift to a more secure lane. Subsequently, a splinebased trajectory generation for this path results in continuous and smooth trajectories. The velocity planner employs an analytical method based on trapezoidal velocity profile to generate velocities for the vehicle traveling along the precomputed path. To provide smooth control, an s-like trapezoidal profile is considered that uses a cubic spline for generating velocities for the ramp-up and ramp-down portions of the curve. The acceleration and velocity constraints, which are derived from road limitations and physical systems, are explicitly considered. Depending upon these constraints and higher module requirements (e.g., maintaining velocity, and stopping), an appropriate segment of the velocity profile is deployed. The motion profiles for all the use-cases are generated and verified graphically.

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