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From Reading to Driving: Priming Mobile Users for Take-over Situations in Highly Automated Driving
- 1. From Reading to Driving: Priming Mobile Users for Take-over Situations in Highly Automated Driving Shadan Sadeghian Borojeni Lars Weber Wilko Heuten Susanne Boll 1
- 3. Levels of Automation (SAE) Level 0: No automation Level 1: Driver assistance Level 2: Occasional self-driving Level 3: Limited self-driving Level 4:Full self-driving under certain conditions Level 5: Full self-driving under all conditions 3
- 4. Take-over Situation 4 Automated Driving Manual Driving Take-Over Request
- 6. Take-over Situation 6 Attention disengagement from non-driving task Shifting attention to manual driving task • Disengage from non-driving task • Perceive and understand the driving environment • Make decisions, and act
- 8. Types of Automation • Information acquisition • Information analysis • Decision making • Actions Parasuramanetal.(2008) Information automation Decision automation
- 9. Types of Automation TORS in L3 Automation Inform drivers of an upcoming take- over situation Provide decision recommendations on appropriate maneuvers Types of Automation Information automation Decision automation
- 10. Research Questions • RQ1: Does conveying recommended decisions through TORs improve performance? • RQ2: Does the level of engagement in NDRT influence responses to TORs? 10
- 11. Experiment 11
- 12. 12 Non-driving Task: Reading Span
- 13. Scenario 13 5s
- 14. 14
- 15. Independent variables • NDRT Engagement: Low vs. High • TOR Type: Information vs. Decision Information Decision Steer/Brake Steer Brake 4 X 1000 Hz pulse duration: 0.25 s IPI = 0.25s 4 X 1000 Hz pulse duration: 0.25 s IPI = 0.25s 4 X 1000 Hz pulse duration: 0.25 s IPI = 0.25s 1 red flash duration: 0.25 s 4 X red flashes duration: 0.25 s IPI = 0.25s 4 X Yellow flashes duration: 0.25 s IPI = 0.25s 15
- 17. Measurements Reaction time (RT) the time between presentation of the TOR and first steering action Time to collision (TTC) to obstacle the time between the first action (steer/brake) and collision to the front vehicle, Workload (NASA TLX) self-reported workload ratings Gaze behavior number and duration of glances at the light display and left side mirror Reading Span overall accuracy and partial credit unit score Number of collisions number of collisions occurred Number of alternative maneuvers • Times were the participants steered when supposed to brake, and braked when supposed to steer 17
- 18. RQ1: Does conveying recommended decisions through TORs improve performance? 18 RTDecision < RTInformation TTCDecision> TTCInformation No significant difference No significant difference (light display/leftmirror) No significant difference No significant difference AltManDecision < AltManInformation
- 19. RQ2: Does the level of engagement in NDRT influence responses to TORs? 19 No significant difference No significant difference TLXeng.high> TLXeng.low No significant difference (light display/left mirror) No significant difference No significant difference No significant difference
- 20. Implications for design of TORs • Presentation modality of TORs should adapt to the modality of the non-driving task • The presentation pattern of audio and visual TORs could be varied to communicate different maneuvers. • Presentation timing of TORs should be context dependent, specifically for traffic situation and upcoming maneuvers. 20
- 21. Conclusion • Conveying decisions about the upcoming takeovers assist drivers to act faster and perform more accurate and safer maneuvers. • Level of NDRT engagement does not influence user responses to TORs. • Tor designs should support decision making but also account for the he reliability of the recommended decisions to the users. 21
Editor's Notes
- The idea of a self-driving car is not new and has been around for over 60 years! However the transition from manual driving to full automatuion does not happen at once!
- National Highway Traffic Safety Administration Society for Automotive Engineering
- HAV users are likely to be heavily engaged in non-driving related tasks . So, less attention will be allocated to driving-related information, such as the vehicle status and driving conditions. Consequently, the situational awareness will be impaired. A take-over situation will require users to rapidly disengage from a non- driving task, shift attention towards the road, perceive and correctly interpret the current driving scenario, decide on the appropriate set of driving maneuvers, and performing them. Increasing levels of automated driving will radically transform the in-vehicle workspace–from one that demands constant vigilance of the driving scene to one that permits users to attend to activities they generally perform on the go such as reading, listening to music, or watching a movie [25]. As a result, they pay less attention to driving-related information which consequently reduces their situational awareness (i.e. perception, cognition, and anticipation of the driving environment).
- Take-over situations require users to disengage from the NDRT, shift their attention to the driving scene, perceive and understand the context, make decisions, and perform appropriate maneuvers accordingly [4]. Therefore, there is a critical need to support users to ensure smooth transitions from their primary task (NDRT) to resuming vehicle control, whenever necessary. Thus, the design of TORs should consider the levels of situational awareness of users at take-over, as well as presentation properties of TORs that ensure fast and safe transitions.
- Prior research has shown that conveying information such as location of road obstacles or situation urgency through TORs assist users in decision making and result in faster and more accurate maneuvers. Moreover, studies conducted in more realistic environments suggest that the design of TORs should focus on supporting decision-making
- Parasuraman et. al have proposed a four-staged taxonomy of automation based on information gathering and analysis Later, Parasuraman defined two Types of automation: information automation as a combination of Stages 1 and 2, and decision automation as a combination of Stages 3 and 4, due to their similarities.
- Parasuraman et. al have proposed a four-staged taxonomy of automation based on information gathering and analysis Later, Parasuraman defined two Types of automation: information automation as a combination of Stages 1 and 2, and decision automation as a combination of Stages 3 and 4, due to their similarities. In this work, we investigated whether applying decision automation at takeovers, i.e. priming users with decisions about appropriate maneuvers through TORs, can improve responses to TORs? We conducted a study in a driving simulator to investigate the effect of decision priming on users’ responses to TORs across different levels of NDRT engagement.
- We investigated three research questions :
- We chose a complex reading span task for the NDRT. This task was chosen due to its similarity to reading, writing text messages, or having a conversation which are expected tasks in an automated driving context. In this task, participants were asked to read unconnected sentences in their native language (i.e. German) and determine whether they semantically made sense. After each sentence, a word was presented to be recalled later. Participants were asked to recall the to-remembered words after presentation of 3 or 5 sentences, and type them in the correct serial order.
- In regular intervals of approximately one minute, the lead vehicle suddenly braked. Depending on the traffic situation the participants had to either brakewait for the approaching car and then overtake, or just steer and overtake. To prevent participants from being able to predict the situation, we designed 6 take-over scenarios 3 for brake, and 3 for steering condition. NDRT reading span task
- Experiment set up : fixed based medium fidelity driving simulator, the NDRt was presented on tablet PC on the laps of the driver. Visual cues from a RGB LED strip behind te steering wheel 60 degrees angle from the fixation on the tablet. Audio cues were presented from speaker behind the driver on two sides and in the front .
- The level of engagement in NDRT is our first independent variable which was in two levels: Low vs. High To vary these levels we carried the difficulty of this task as suggested by previous works. Specifications of audio-visual TORs for different types of automation and recommended maneuvers (IPI: interpulse interval). Information: all LEDs light up, audio cuesfrom both loud speakers behind the driver. Decision-Brake: LEDs light up from center in an expanding pattern, audio cues from the speaker in front of the driver. Decision-Steer: LEDs light up iteratively moving from right to left, audio cues from both loud speakers behind the driver moving from right to left.
- Second trial only
- Measures
- Yes! Users responded faster to TORs when they were primed with decisions, and showed smaller TTC values as well. TLX = that even though in decision automation, TORs conveyed more information to the users, they did not influence the perceived workload ratings. Eyegaze:= the decision automation did not cause users to look less often at the mirror or more often at light display to perceive the traffic situation, and conveyed decisions RT = We did not find a significant two-way interaction between NDRT engagement and automation type TTC=No significant two-way interaction was observed between NDRT engagement and automation type Alternative Maneuvers= Participants had significantly more alternative steering maneuvers in information than decision automation conditions.
- The answer is no! Although we observed a significant difference on the workload ratings with an increase in the high-eng condition, no effect of NDRT eng. On TOR respopnses was obsereved in any of our measures. Eyegaze=users did not need to look less often at the light display or the left mirror when their NDRT engagement was high.
- Time For now, we recommend presenting TORs earlier when a vehicle is traveling in road networks that necessitate frequent turnings, OR PreAlrets suggested by van der heiden Modality to avoid conflicts between the demands of non-driving activities and the capacity for users to detect and respond appropriately to TORs.
- Time For now, we recommend presenting TORs earlier when a vehicle is traveling in road networks that necessitate frequent turnings, OR PreAlrets suggested by van der heiden Modality to avoid conflicts between the demands of non-driving activities and the capacity for users to detect and respond appropriately to TORs.