Road Safety: Human Factors Aspects of Intelligent Vehicle Technologies

Olaverri-Monreal, Cristina

doi:10.1007/978-3-030-02907-4_16

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…In the near future, we can expect autonomous and manually driven cars to coexist on most roads. Drivers operating conventional vehicles might expect human-like behavior from autonomous vehicles, which could cause situations of uncertainty and mistrust [ 4 ] if the expectations are not fulfilled, and this could ultimately threaten road safety [ 5 ]. This is particularly important in certain complex scenarios such as lane-changing maneuvers, in which cooperation with other road users is required.…”

Section: Introductionmentioning

confidence: 99%

A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Smirnov

Liu

Validi

et al. 2021

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Smirnov

Liu

Validi

et al. 2021

Sensors

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“…Although the popularity of automobiles has introduced considerable convenience to people, it has also caused a numerous traffic safety problems that cannot be ignored, such as traffic congestion and frequent road accidents. Traffic safety issues are largely caused by subjective reasons related to the driver, such as inattention, improper driving operation and non-compliance with traffic rules, and smart cars have become an effective means to eliminate these human factors [1,2,3,4,5]. Self-driving technology can assist, or even independently complete the driving operation, which is of remarkable importance to liberate the human body and considerably reduce the incidence of accidents [6,7].…”

Section: Introductionmentioning

confidence: 99%

Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles

Cao

Song

Peng

et al. 2019

Sensors

102

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Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.

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“…The potential boredom and road monotony associated with higher levels of driving automation might lead to a reduction in driver situational awareness [14,15]. This hypovigilance needs to be taken into account when a vehicle control is expected from the driver [16].…”

mentioning

confidence: 99%

“…Most relevant standards, guidelines and regulations for the design and implementation of automated vehicle features in Intelligent Transportation Systems (ITS) and factors that relate to TOR (adapted and extended from[15]). …”

mentioning

confidence: 99%

Automated Driving: A Literature Review of the Take over Request in Conditional Automation

Morales-Alvarez

Sipele

Léberon³

et al. 2020

Electronics

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In conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the vehicle operates within its Operational Design Domain (ODD). Outside the ODD, a safe transition process from the ADS engaged mode to manual driving should be initiated by the system through the issue of an appropriate Take Over Request (TOR). In this case, the driver’s state plays a fundamental role, as a low attention level might increase driver reaction time to take over control of the vehicle. This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process. It introduces the topic in the appropriate context describing as well a variety of concerns that are associated with the TOR. It also provides theoretical foundations on implemented designs, and report on concrete examples that are targeted towards designers and the general public. Moreover, it compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research, discussing also approaches and limitations and providing conclusions.

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Road Safety: Human Factors Aspects of Intelligent Vehicle Technologies

Cited by 10 publications

References 26 publications

A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles

Automated Driving: A Literature Review of the Take over Request in Conditional Automation

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