Left or Right: Auditory Collision Warnings for Driving Assistance Systems

Author(s):  
Edin Sabic ◽  
Jing Chen

Assistance driving systems aim to facilitate human behavior and increase safety on the road. These systems comprise common systems such as forward collision warning systems, lane deviation warning systems, and even park assistance systems. Warning systems can communicate with the driver through various modalities, but auditory warnings have the advantage of not further tasking visual resources that are primarily used for driving. Auditory warnings can also be presented from a certain location within the cab environment to be used by the driver as a cue. Beattie, Baillie, Halvey, and McCall (2014) assessed presenting warnings in stereo configuration, coming from one source, and bilateral configuration, panned fully from left or right, and found that drivers felt more in control with lateral warnings than stereo warnings when the car was in self-driving mode. Straughn, Gray, and Tan (2009) examined laterally presented auditory warnings to signal potential collisions. They found that the ideal presentation of warnings in either the avoidance direction, in which the driver should direct the car to avoid a collision, or the collision direction, in which the potential collision is located, was dependent on time to collision. Wang, Proctor, and Pick (2003) applied the stimulus-response compatibility principle to auditory warning design by using a steering wheel in a non-driving scenario and found that a tone presented monaurally in the avoidance-direction led to the fastest steering response. However, the reverse finding occurred when similar experiments utilized a driving simulator in a driving scenario (Straughn et al., 2009; Wang, Pick, Proctor, & Ye, 2007). The present study further investigated how to design spatially presented auditory collision warnings to facilitate drivers’ response to potential collisions. Specifically, tones indicating a pedestrian walking across the road were presented either in the avoidance direction or in the collision direction. The experimental task consisted of monitoring the road for potential collisions and turning the wheel in the appropriate direction to respond. Additionally, time to collision was manipulated to investigate the impact of the timing of the warning and increasing time pressure on the steering response. Time to collision was manipulated by half second intervals from two to four seconds resulting in five different time-to-collision scenarios. Lastly, the effect of individual differences in decision-making styles were also considered by using two decision-making style questionnaires. Results from the experiment showed that the presentation of a collision warning in the collision direction led to faster responses when compared to the warning in the avoidance direction. This result may be due to the collision warning directing the attention of the participant to the location of the threat so that they can more quickly make a response decision. Further, the advantage of avoidance-direction warnings over collision-direction warnings was greater with greater time to collision. Results showed that participant responses to varying time to collision influenced their reaction time. The participants appeared to have not relied solely on the auditory tones, but rather they utilized the warning tones in conjunction with visual information in the environment. These results from this study have implications for improving collision avoidance systems: Presentation of a collision warning in the direction of the collision may be more intuitive to drivers, regardless of time to collision.

Author(s):  
Hamed Mozaffari ◽  
Ali Nahvi

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.


2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Lian Hou ◽  
Jingliang Duan ◽  
Wenjun Wang ◽  
Renjie Li ◽  
Guofa Li ◽  
...  

Bicycling is one of the popular modes of transportation, but bicyclists are easily involved in injuries or fatalities in vehicle-bicycle (V-B) accidents. The AEB (Autonomous Emergency Braking) systems have been developed to avoid collisions, but their adaptiveness needs to be further improved under different motion patterns of V-B conflicts. This paper analyzes drivers’ braking behaviors in different motion patterns of V-B conflicts to improve the performance of Bicyclist-AEB systems. For safety and data reliability, a driving simulator was used to reconstruct two typical conflict types, i.e., SCR (a bicycle crossing the road from right in front of a straight going car) and SSR (a bicycle cut-in from right in front of a straight going car). Either conflict contained various parameterized motion patterns, which were characterized by a combination of parameters: Vc (car velocity), TTC (time-to-collision), Vb (bicycle velocity), and Dlat (lateral distance between the car and the bicycle) or Vlat (maximum lateral velocity of the bicycle). Some 26 licensed drivers participated in an orthogonal experiment for braking behavior analysis. Results revealed that drivers brake immediately when V-B conflicts occur; hence the BRT (brake reaction time) is independent of any motion pattern parameters. This was further verified by another orthogonal experiment with 10 participants using the eye tracking device. BRT in SSR is longer than that in SCR due to the less perceptible risk and drivers’ lower expectation of a collision. The braking intensity and brake Pedal Speed are higher in short-TTC patterns in both conflict types. Therefore, TTC is not a proper activation threshold but a reasonable indicator of braking intensity and Pedal Speed for driver-adaptive AEB systems. By applying the findings in the Bicyclist-AEB, the adaptiveness and acceptability of Bicyclist-AEB systems can be improved.


Author(s):  
Anshuman Sharma ◽  
Zuduo Zheng ◽  
Jiwon Kim ◽  
Ashish Bhaskar ◽  
Md. Mazharul Haque

Response time (RT) is a critical human factor that influences traffic flow characteristics and traffic safety, and is governed by drivers’ decision-making behavior. Unlike the traditional environment (TE), the connected environment (CE) provides information assistance to drivers. This in-vehicle informed environment can influence drivers’ decision-making and thereby their RTs. Therefore, to ascertain the impact of CE on RT, this study develops RT estimation methodologies for TE (RTEM-TE) and CE (RTEM-CE), using vehicle trajectory data. Because of the intra-lingual inconsistency among traffic engineers, modelers, and psychologists in the usage of the term RT, this study also provides a ubiquitous definition of RT that can be used in a wide range of applications. Both RTEM-TE and RTEM-CE are built on the fundamental stimulus–response relationship, and they utilize the wavelet-based energy distribution of time series of speeds to detect the stimulus–response points. These methodologies are rigorously examined for their efficiency and accuracy using noise-free and noisy synthetic data, and driving simulator data. Analysis results demonstrate the excellent performance of both the methodologies. Moreover, the analysis shows that the mean RT in CE is longer than the mean RT in TE.


Author(s):  
Udai Hassein ◽  
Maksym Diachuk ◽  
Said Easa

Passing collisions are one of the most serious traffic safety problems on two-lane highways. These collisions occur when a driver overestimates the available sight distance. This paper presents a framework for a passing collision warning system (PCWS) that assists drivers in avoiding passing collisions by reducing the likelihood of human error. The system uses a combination of a camera and radar sensors to identify the impeding vehicle type and to detect the opposing vehicles traveling in the left lane. The study involved the development of a steering control model providing lane-change maneuvers, the design of a driving simulator experiment that allows for the collection of data necessary to estimate passing parameters, and the elaboration of the algorithm for the PCWS based on sensor signals to detect impeding vehicles such as trucks. Simulation tests were carried out to confirm the effectiveness of the proposed PCWS algorithm. The impact of driver behavior on passing maneuvers was also investigated. Mathematical and imitation models were enhanced to implement Simulink for replications of real-life driving scenarios. The different factors that affect system accuracy were also examined.


Author(s):  
West M. O’Brien ◽  
Xingwei Wu ◽  
Linda Ng Boyle

Collision warning systems alert drivers of potential safety hazards. Forward collision warning (FCW) systems have been widely implemented and studied. However, intersection collision warning systems (ICWS), such as intersection movement assist (IMA), are more complex. Additional studies are needed to identify the best alert for directing the driver toward the hazard. A driving simulator study with 48 participants was conducted to examine three speech-based auditory alerts (general, directional, and command) in a simulated red light running (RLR) collision scenario. The command alert that informed the drivers to brake was the most effective in reducing the number of collisions. The post-drive questionnaire showed that drivers also rated the brake alert to be best in terms of interpretation (based on the Kruskal Wallis test). This study provides insight into the performance of different types of speech-based alerts for an intersection collision warning system and can provide guidance for future studies.


Author(s):  
Missie Smith ◽  
Kiran Bagalkotkar ◽  
Joseph L. Gabbard ◽  
David R. Large ◽  
Gary Burnett

Objective We controlled participants’ glance behavior while using head-down displays (HDDs) and head-up displays (HUDs) to isolate driving behavioral changes due to use of different display types across different driving environments. Background Recently, HUD technology has been incorporated into vehicles, allowing drivers to, in theory, gather display information without moving their eyes away from the road. Previous studies comparing the impact of HUDs with traditional displays on human performance show differences in both drivers’ visual attention and driving performance. Yet no studies have isolated glance from driving behaviors, which limits our ability to understand the cause of these differences and resulting impact on display design. Method We developed a novel method to control visual attention in a driving simulator. Twenty experienced drivers sustained visual attention to in-vehicle HDDs and HUDs while driving in both a simple straight and empty roadway environment and a more realistic driving environment that included traffic and turns. Results In the realistic environment, but not the simpler environment, we found evidence of differing driving behaviors between display conditions, even though participants’ glance behavior was similar. Conclusion Thus, the assumption that visual attention can be evaluated in the same way for different types of vehicle displays may be inaccurate. Differences between driving environments bring the validity of testing HUDs using simplistic driving environments into question. Application As we move toward the integration of HUD user interfaces into vehicles, it is important that we develop new, sensitive assessment methods to ensure HUD interfaces are indeed safe for driving.


Author(s):  
Hananeh Alambeigi ◽  
Anthony D. McDonald

Objective This study investigates the impact of silent and alerted failures on driver performance across two levels of scenario criticality during automated vehicle transitions of control. Background Recent analyses of automated vehicle crashes show that many crashes occur after a transition of control or a silent automation failure. A substantial amount of research has been dedicated to investigating the impact of various factors on drivers’ responses, but silent failures and their interactions with scenario criticality are understudied. Method A driving simulator study was conducted comparing scenario criticality, alert presence, and two driving scenarios. Bayesian regression models and Fisher’s exact tests were used to investigate the impact of alert and scenario criticality on takeover performance. Results The results show that silent failures increase takeover times and the intensity of posttakeover maximum accelerations and decrease the posttakeover minimum time-to-collision. While the predicted average impact of silent failures on takeover time was practically low, the effects on minimum time-to-collision and maximum accelerations were safety-significant. The analysis of posttakeover control interaction effects shows that the effect of alert presence differs by the scenario criticality Conclusion Although the impact of the absence of an alert on takeover performance was less than that of scenario criticality, silent failures seem to play a substantial role—by leading to an unsafe maneuver—in critical automated vehicle takeovers. Application Understanding the implications of silent failure on driver’s takeover performance can benefit the assessment of automated vehicles’ safety and provide guidance for fail-safe system designs.


Vehicles ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 764-777
Author(s):  
Dario Niermann ◽  
Alexander Trende ◽  
Klas Ihme ◽  
Uwe Drewitz ◽  
Cornelia Hollander ◽  
...  

The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling of discomfort of human passengers while driving autonomously and the automatic detection of this discomfort with several model approaches, using the combination of different data sources. Based on a driving simulator study, we analyzed the discomfort reports of 50 participants for autonomous inner city driving. We found that perceived discomfort depends on the driving scenario (with discomfort generally peaking in complex situations) and on the passenger (resulting in interindividual differences in reported discomfort extend and duration). Further, we describe three different model approaches on how to predict the passenger discomfort using data from the vehicle’s sensors as well as physiological and behavioral data from the passenger. The model’s precision varies greatly across the approaches, the best approach having a precision of up to 80%. All of our presented model approaches use combinations of linear models and are thus fast, transparent, and safe. Lastly, we analyzed these models using the SHAP method, which enables explaining the models’ discomfort predictions. These explanations are used to infer the importance of our collected features and to create a scenario-based discomfort analysis. Our work demonstrates a novel approach on passenger state modelling with simple, safe, and transparent models and with explainable model predictions, which can be used to adapt the vehicles’ actions to the needs of the passenger.


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