Driver Involvement in Lane-Change Decision Making for Maintaining Situation Awareness during Automated Driving

2016 ◽  
Vol 9 (6) ◽  
pp. 257-263 ◽  
Author(s):  
Takaya YAMASHITA ◽  
Makoto ITOH
Author(s):  
Mohsen Rafat ◽  
Shahram Azadi

Trajectory planning with consideration of surrounding vehicles and decision-making in the middle of complicated manoeuvres are some of the most important challenges regarding the implementation of automated driving. Since transient dynamic traffic conditions was limited to the start of the manoeuvre in previous research, no solution was provided for the surrounding vehicles’ transient changes during the manoeuvre. The algorithm presented in this paper is able to adapt to unstable variable traffic conditions and it is robust to changes in the surrounding vehicles’ conditions, even during the lane change manoeuvre. The Adaptive Lane Change algorithm provides all possible safe trajectories for any moment of manoeuvre via applying DE optimization method on a fifth-order polynomial equation. In this way, it is able to make a new decision and plan safe trajectories according to the new conditions of surrounding vehicles during the manoeuvre. Also, it guarantees collision avoidance at all-time via simultaneous longitudinal and lateral vehicle control. Improving the trajectory during a lane change manoeuvre regarding the surrounding vehicles’ conditions is considered as one of the main contributions of the presented algorithm. A second main contribution is the collision avoidance considering the vehicle’s dynamic via returning to the initial lane when there is no safe trajectory in the target lane, even during the lane change manoeuvre. The decision-making unit is evaluated by real driving tests. Then, the whole structure is simulated with MATLAB in complex transient dynamic traffic conditions via various scenarios and its performance is tested with IPG CarMaker in the presence of simulated surrounding vehicles.


Author(s):  
A. V. Smirnov ◽  
T. V. Levashova

Introduction: Socio-cyber-physical systems are complex non-linear systems. Such systems display emergent properties. Involvement of humans, as a part of these systems, in the decision-making process contributes to overcoming the consequences of the emergent system behavior, since people can use their experience and intuition, not just the programmed rules and procedures.Purpose: Development of models for decision support in socio-cyber-physical systems.Results: A scheme of decision making in socio-cyber-physical systems, a conceptual framework of decision support in these systems, and stepwise decision support models have been developed. The decision-making scheme is that cybernetic components make their decisions first, and if they cannot do this, they ask humans for help. The stepwise models support the decisions made by components of socio-cyber-physical systems at the conventional stages of the decision-making process: situation awareness, problem identification, development of alternatives, choice of a preferred alternative, and decision implementation. The application of the developed models is illustrated through a scenario for planning the execution of a common task for robots.Practical relevance: The developed models enable you to design plans on solving tasks common for system components or on achievement of common goals, and to implement these plans. The models contribute to overcoming the consequences of the emergent behavior of socio-cyber-physical systems, and to the research on machine learning and mobile robot control.


2009 ◽  
Author(s):  
Robert J. Pleban ◽  
Jennifer S. Tucker ◽  
Vanessa Johnson Katie /Gunther ◽  
Thomas R. Graves

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 162
Author(s):  
Soyeon Kim ◽  
René van Egmond ◽  
Riender Happee

In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance in higher automation levels allowing drivers to take their eyes off the road (SAE3 and SAE4). We categorized user interface (UI) factors from an automated vehicle-related information perspective. Short take-over times are consistently associated with take-over requests (TORs) initiated by the auditory modality with high urgency levels. On the other hand, take-over requests directly displayed on non-driving-related task devices and augmented reality do not affect take-over time. Additional explanations of take-over situation, surrounding and vehicle information while driving, and take-over guiding information were found to improve situational awareness. Hence, we conclude that advanced user interfaces can enhance the safety and acceptance of automated driving. Most studies showed positive effects of advanced UI, but a number of studies showed no significant benefits, and a few studies showed negative effects of advanced UI, which may be associated with information overload. The occurrence of positive and negative results of similar UI concepts in different studies highlights the need for systematic UI testing across driving conditions and driver characteristics. Our findings propose future UI studies of automated vehicle focusing on trust calibration and enhancing situation awareness in various scenarios.


Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.


Author(s):  
HyunJoo Park ◽  
HyunJae Park ◽  
Sang-Hwan Kim

In conditional automated driving, drivers may be required starting manual driving from automated driving mode after take-over request (TOR). The objective of the study was to investigate different TOR features for drivers to engage in manual driving effectively in terms of reaction time, preference, and situation awareness (SA). Five TOR features, including four features using countdown, were designed and evaluated, consisted of combinations of different modalities and codes. Results revealed the use of non-verbal sound cue (beep) yielded shorter reaction time while participants preferred verbal sound cue (speech). Drivers' SA was not different for TOR features, but the level of SA was affected by different aspects of SA. The results may provide insights into designing multimodal TOR along with drivers' behavior during take-over tasks.


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