Driver intention prediction using model-added Bayesian network

2020 ◽  
pp. 095440702096896
Author(s):  
Ruitao Song
Keyword(s):  
Bayesian Network ◽  
Autonomous Driving ◽  
Driver Model ◽  
Prediction Algorithm ◽  
Data Sets ◽  
Lane Change ◽  
Potential Threat ◽  
Surrounding Environment ◽  
Reliable Detection ◽  
Driver’S Intention

The autonomous driving technology requires reliable detection and prediction of the surrounding environment. Predicting the lane change intention of the surrounding traffic is critical to evaluate the potential threat around the host vehicle. This paper develops a lane change maneuver prediction algorithm based on a newly proposed driver model combined with a Bayesian network. The innovation of the proposed algorithm is the utilization of the driver model while calibrating and executing the Bayesian network. The prediction algorithm can provide not only the driver’s intention but also the probability associated with the intention. The Next-Generation Simulation data sets are used to develop and validate the prediction model. In total, there are more than 2000 lane change events used in this paper. The result shows that the proposed prediction algorithm can provide an accurate prediction of the surrounding vehicle’s lane change maneuver.

scholarly journals Incorporation of the Driver’s Personality Profile in an Agent Model

2015 ◽  
Vol 27 (6) ◽  
pp. 505-514 ◽  
Author(s):  
Mian Muhammad Mubasher ◽  
Syed Waqar Ul Qounain Jaffry
Keyword(s):  
Traffic Flow ◽  
Serious Games ◽  
Urban Traffic ◽  
Driver Model ◽  
Big Five Personality ◽  
Personality Profile ◽  
Traffic Planning ◽  
Agent Model ◽  
Root Cause ◽  
Surrounding Environment

Urban traffic flow is a complex system. Behavior of an individual driver can have butterfly effect which can become root cause of an emergent phenomenon such as congestion or accident. Interaction of drivers with each other and the surrounding environment forms the dynamics of traffic flow. Hence global effects of traffic flow depend upon the behavior of each individual driver. Due to several applications of driver models in serious games, urban traffic planning and simulations, study of a realistic driver model is important. Hhence cognitive models of a driver agent are required. In order to address this challenge concepts from cognitive science and psychology are employed to design a computational model of driver cognition which is capable of incorporating law abidance and social norms using big five personality profile.

Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving

2018 ◽  
Vol 3 (3) ◽  
pp. 276-286 ◽  
Author(s):  
Yihuan Zhang ◽  
Qin Lin ◽  
Jun Wang ◽  
Sicco Verwer ◽  
John M. Dolan
Keyword(s):  
Autonomous Driving ◽  
Lane Change ◽  
Car Following ◽  
Intention Estimation

A Comparison of Trajectory Planning and Control Frameworks for Cooperative Autonomous Driving

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Icaro Bezerra Viana ◽  
Husain Kanchwala ◽  
Kenan Ahiska ◽  
Nabil Aouf
Keyword(s):  
Trajectory Planning ◽  
Cooperative Behavior ◽  
Autonomous Driving ◽  
General Idea ◽  
Mixed Integer ◽  
Quadratic Program ◽  
Change Scenario ◽  
Planning Problem ◽  
Lane Change ◽  
Double Lane Change

Abstract This work considers the cooperative trajectory-planning problem along a double lane change scenario for autonomous driving. In this paper, we develop two frameworks to solve this problem based on distributed model predictive control (MPC). The first approach solves a single nonlinear MPC problem. The general idea is to introduce a collision cost function in the optimization problem at the planning task to achieve a smooth and bounded collision function, and thus to prevent the need to implement tight hard constraints. The second method uses a hierarchical scheme with two main units: a trajectory-planning layer based on mixed-integer quadratic program (MIQP) computes an on-line collision-free trajectory using simplified motion dynamics, and a tracking controller unit to follow the trajectory from the higher level using the nonlinear vehicle model. Connected and automated vehicles (CAVs) sharing their planned trajectories lay the foundation of the cooperative behavior. In the tests and evaluation of the proposed methodologies, matlab-carsim cosimulation is utilized. carsim provides the high-fidelity model for the multibody vehicle dynamics. matlab-carsim conjoint simulation experiments compare both approaches for a cooperative double lane change maneuver of two vehicles moving along a one-way three-lane road with obstacles.

Evaluation of Bayesian Network Structure Learning Using Elephant Swarm Water Search Algorithm

2020 ◽  
pp. 139-159
Author(s):  
Shahab Wahhab Kareem ◽  
Mehmet Cudi Okur
Keyword(s):  
Simulated Annealing ◽  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Search Algorithm ◽  
Confusion Matrix ◽  
Data Sets ◽  
Greedy Search ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning which can represent probabilistic dependency relationships among the variables. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) for Bayesian network structure learning. In the algorithm; Deleting, Reversing, Inserting, and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly, water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with Pigeon Inspired Optimization, Simulated Annealing, Greedy Search, Hybrid Bee with Simulated Annealing, and Hybrid Bee with Greedy Search using BDeu score function as a metric for all algorithms. They investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of evaluations, the proposed algorithm achieves better performance than the other algorithms and produces better scores as well as the better values.

scholarly journals Estimating Autonomous Vehicle Localization Error Using 2D Geographic Information

2019 ◽  
Vol 8 (6) ◽  
pp. 288 ◽  
Author(s):  
Kelvin Wong ◽  
Ehsan Javanmardi ◽  
Mahdi Javanmardi ◽  
Shunsuke Kamijo
Keyword(s):  
Point Cloud ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Geographic Information ◽  
Localization Error ◽  
Random Forest Regression ◽  
Vehicle Localization ◽  
Surrounding Environment ◽  
Tightly Coupled ◽  
Critical Requirement

Accurately and precisely knowing the location of the vehicle is a critical requirement for safe and successful autonomous driving. Recent studies suggest that error for map-based localization methods are tightly coupled with the surrounding environment. Considering this relationship, it is therefore possible to estimate localization error by quantifying the representation and layout of real-world phenomena. To date, existing work on estimating localization error have been limited to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D geographic information datasets as a proxy to estimate autonomous vehicle localization error. Seven map evaluation factors were defined for 2D geographic information in a vector format, and random forest regression was used to estimate localization error for five experiment paths in Shinjuku, Tokyo. In the best model, the results show that it is possible to estimate autonomous vehicle localization error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm.

A motivational driver model for the design of a rear-end crash avoidance system

2019 ◽  
Vol 234 (1) ◽  
pp. 10-26
Author(s):  
Hamed Mozaffari ◽  
Ali Nahvi
Keyword(s):  
Driving Simulator ◽  
Human Subjects ◽  
Autonomous Driving ◽  
Driver Model ◽  
Driver Assistance Systems ◽  
Optimal Point ◽  
Time To Collision ◽  
Human Aspects ◽  
Crash Avoidance ◽  
Assistance Systems

A motivational driver model is developed to design a rear-end crash avoidance system. Current driver assistance systems use engineering methods without considering psychological human aspects, which leads to false activation of assistance systems and complicated control algorithms. The presented driver model estimates driver’s psychological motivations using the combined longitudinal and lateral time to collision, the vehicle kinematics, and the vehicle dynamics. These motivations simplify both autonomous driving algorithms and human-machine interactions. The optimal point of a motivational multi-objective cost function defines the decision for the autonomous driving. Moreover, the motivations are used as risk assessment factors for driver–machine interaction in dangerous situations. The system is evaluated on 10 human subjects in a driving simulator. The assistance system had no false activation during the tests. It avoided collisions in all the rear-end crash avoidance scenarios, while 90% of human subjects did not.

scholarly journals Numerical Precision Effects on GPU Simulation of Massive Spatial Data, Based on the Modified Planar Rotator Model

2020 ◽  
Vol 226 ◽  
pp. 02015
Author(s):  
Matúš Lach ◽  
Michal Borovský ◽  
Milan Žukovič
Keyword(s):  
Spatial Data ◽  
Statistical Physics ◽  
Parallel Implementation ◽  
Prediction Method ◽  
Spatial Prediction ◽  
Spin Model ◽  
Prediction Algorithm ◽  
Data Sets ◽  
Spatial Correlations ◽  
Spin Pair

The present research builds on a recently proposed spatial prediction method for discretized two-dimensional data, based on a suitably modified planar rotator (MPR) spin model from statistical physics. This approach maps the measured data onto interacting spins and, exploiting spatial correlations between them, which are similar to those present in geostatistical data, predicts the data at unmeasured locations. Due to the shortrange nature of the spin pair interactions in the MPR model, parallel implementation of the prediction algorithm on graphical processing units (GPUs) is a natural way of increasing its efficiency. In this work we study the effects of reduced computing precision as well as GPU-based hardware intrinsic functions on the speedup and accuracy of the MPR-based prediction and explore which aspects of the simulation can potentially benefit the most from the reduced precision. It is found that, particularly for massive data sets, a thoughtful precision setting of the GPU implementation can significantly increase the computational efficiency, while incurring little to no degradation in the prediction accuracy.

Automatically Building Linking Relations between Lane-Level Map and Commercial Navigation Map Using Topological Networks Matching

2020 ◽  
Vol 73 (5) ◽  
pp. 1159-1178
Author(s):  
Lu Tao ◽  
Pan Zhang ◽  
Lixin Yan ◽  
Dunyao Zhu
Keyword(s):  
Real Data ◽  
Autonomous Driving ◽  
Data Sets ◽  
Driving System ◽  
Forward Path ◽  
Level Information ◽  
Data Source ◽  
System Mapping ◽  
Autonomous Driving System ◽  
Mapping Information

The lane-level map, which contains the lane-level information severely lacking in widely used commercial navigation maps, has become an essential data source for autonomous driving systems. The linking relations between lane-level map and commercial navigation map can facilitate an autonomous driving system mapping information between different applications using different maps. In this paper, an approach is proposed to build the linking relations automatically. The different topology networks are first reconstructed into similar structures. Then, to build the linking relations automatically, the adaptive multi-filter algorithm and forward path exploring algorithm are proposed to detect corresponding junctions and paths, respectively. The approach is validated by two real data sets of more than 150 km of roads, mainly highway. The linking relations for nearly 94% of the total road length have been built successfully.

Brain Limbic System Based Robust Control for Lane Change Maneuver

2010 ◽  
Author(s):  
Changwon Kim ◽  
Reza Langari
Keyword(s):  
Intelligent Control ◽  
Limbic System ◽  
Driver Model ◽  
Lane Change ◽  
Lateral Dynamics ◽  
Human Driver ◽  
Control Scheme ◽  
Suggested Strategy ◽  
Simulation Results ◽  
The Brain

This paper presents the application of a novel intelligent control strategy for lane change maneuvers in highway environment. The lateral dynamics of a vehicle with and without wind disturbance are derived and utilized to implement a neuromophic controller based on the brain limbic system. To show the robustness of the proposed controller, several disturbance conditions including wind, uncertainty in the cornering stiffness, and changes in the vehicle mass, are investigated. To demonstrate the performance of the suggested strategy, the simulation results of the proposed method were compared with the human driver model based control scheme, which has been discussed in the literature. The simulation results demonstrate the superiority of the proposed controller in energy efficiency, driving comfort, and robustness.

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