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    <title>Research in Progress (RIP)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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      <title>Trust-building in L4 Automation by Intent Communication: Using HMI Design to Examine Passengers' Trust and Acceptance of Automated Vehicles</title>
      <link>https://rip.trb.org/View/2472699</link>
      <description><![CDATA[The rapid advancement of automated vehicle (AV) technology requires effective communication between AVs and their passengers to foster trust, perceived safety, and acceptance. This project explores how intent communication via Human-Machine Interfaces (HMIs) affects passenger trust in SAE Level 4 AVs. Using a driving simulator, two interface designs will be tested for their ability to convey vehicle sensing, navigation, and control intent. Outcomes include quantitative and qualitative data on passenger trust, usability, and technology acceptance, with the potential to inform HMI development for broader AV deployment. The findings aim to enhance AV adoption, improve passenger experience, and support safer integration of automated systems into transportation networks.]]></description>
      <pubDate>Mon, 09 Dec 2024 10:21:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2472699</guid>
    </item>
    <item>
      <title>Safe Decision-Making in Interactive Environments</title>
      <link>https://rip.trb.org/View/2292665</link>
      <description><![CDATA[Autonomous ground vehicles must safely operate in highly interactive environments with human uncertainties. Safe actions depend on context, interactions, and absolute (mathematical measures for safety) may differ from how humans perceive as safe and reliable behaviors. However, producing context-dependent and interaction-aware safe actions is non-trivial due to the following technical challenges.   Challenge 1: Hardness in the identification of interaction mechanisms. When interaction mechanisms are known or can be learned, many safe learning and control techniques can be employed. However, in many interactive environments, it may be fundamentally difficult to fully identify the mechanisms of the opponent's interaction due to unobserved confounders.   Challenge 2: Latent risk and latent variables. There exist latent risks (such as occlusions) and unobserved variables (e.g. awareness and intention), and safe actions depend on such factors. For example, pedestrians can decide to enter a crosswalk, but such intent may not be directly observable.  Decisions that ignore such factors may experience unexpected risks.   Challenge 3: Tensions between long-term safety vs. computation. Accounting for long-term outcomes in risk quantification and control is challenging due to stringent computation vs. time-horizon tradeoffs, particularly for rare events. Myopic safety can be efficiently certified, but ensuring long-term safety may require prohibitive computation.  Although distribution shifts are often approached by finding actions that are robust to changes or avoid changes, much less work explores how to proactively induce desirable opponent behavior changes in interactive environments. For example, whereas humans can slowly squeeze their way through crowded environments, autonomous systems programmed to maintain worst-case distance may not find feasible solutions (e.g., freezing robot problems). Proximity can be safe or risky depending on opponents’ interaction mechanisms, but this is not captured in a simple risk measure of distance. The proposed research aims at realizing such capabilities by accounting for interaction models and anticipating unobserved unknowns in risk quantification and decision-making. Specifically, the research team proposes the following research.    Thrust 1: The research team will establish an efficient risk quantification method with theoretical guarantees. The research team will leverage an integrative view of stochastic systems, MC, PDEs, and Physics-informed neural networks (PINNs) to estimate intervention risk from heterogeneous data and exploit low-dimensional structures. The research team's prior work has derived four types of long-term safe probabilities as unique solutions to deterministic linear PDEs, which characterize the relation between risk probabilities of different time horizons and initial states. PINNs with these PDE constraints have been shown to be able to infer risk probabilities beyond available data with provable generalization. Here, the research team will explore such characterization and enable long-term risk to be quantified using shorter-term interaction data.   Thrust 2: The research team will develop efficient long-term safety certificates for interactive environments. While treating statistical models as mechanistic models may neglect important latent risk factors, little effort has been made to rigorously differentiate the underlying mechanistic models vs. observed statistical models in the design of safety certificates. Here, the research team will build upon their prior work on probabilistic invariance to differentiate the two models, control the risk probability using observed statistics, and handle information constraints arising from delayed and rate-limited communication.]]></description>
      <pubDate>Tue, 21 Nov 2023 18:30:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292665</guid>
    </item>
    <item>
      <title>Designing In-vehicle Message Delivery for Manual and Highly Automated Driving</title>
      <link>https://rip.trb.org/View/1723541</link>
      <description><![CDATA[With the rapid development of sensor and computing technologies, personal vehicles are now capable of collecting voluminous information on vehicle status and the road environment, as well as making proximity estimates and predicting potential driving events. Recent advances in vehicle automation have envisioned future driving without the need for drivers to attend to the road. With these trends in vehicle technology for the driving task, a shift in information communication is taking place from driver-roadway interaction to driver and in-vehicle display interaction. • Compared to (SAE) Level 2, drivers under Level 3 automation were less accurate in logo identification, likely due to a reduced number of glances to on-road signs. 

Despite decades of research on in-vehicle notification display designs, the majority of studies have concentrated on presenting information related to the driving task, such as display of collision warnings and navigation information. There is little knowledge on how to effectively present information that is secondary to driving but important for a trip, such as notifications of a rest area and local businesses. This information is conventionally presented on a guide or logo sign. Furthermore, existing research on in-vehicle information presentation during highly automated driving has only focused on safety critical messages such collision warnings. These studies do not necessarily generalize to notifications that are trip-related but non-safety critical information, as driver attentional processing could differ depending on the degree of relevance of the notification to the driving task and under various levels of automation. 

This project examined the influence of in-vehicle dynamic message displays of trip-related but non-safety critical information on driver visual behavior and driving performance, as compared with conventional on-road guide or logo sign use, during manual and highly automated driving. To achieve this goal, we first conducted a literature review on the following topics: (1) advances in content and update rates of in-vehicle trip-related messaging, (2) driver interaction with autonomous vehicle technology, (3) driver alertness and information processing, and (4) human factors issues in design of driver notification systems. The research team also performed two empirical studies using the NCSU advanced driving simulator, with the first experiment (E1) examining how drivers respond to messages posing various information loads during manual driving and the second (E2) investigating driver responses to messages when driving with high-level automation. 

Our findings support the use of in-vehicle displays, especially in combination with on-road signage. 

Under manual driving: 
Driver reactions to road hazards were slower when logos were present but the number of collisions did not increase. 
The use of in-vehicle displays produced better vehicle control with comparable workload and visual distraction, as compared to on-road signage. 
Simultaneous in-vehicle and on-road displays showed a benefit on hazard negotiation (fewer collisions). 
Some age differences were observed in driving and visual behaviors, but the evidence does not suggest any particular age-related safety concerns. 
​
When driving with partial automation (level 2): 
Simultaneous in-vehicle and on-road displays led to the highest logo identification accuracy and little impairment of hazard negotiation when logos were present. 
Simultaneous in-vehicle and on-road displays led to shorter single off-road glance durations and mitigated the effect of information load on driver visual processing. Drivers made fewer but longer glances to on-road signage, as compared to in-vehicle displays. 
Older drivers were less accurate in logo identification than young and middle-aged drivers. However, all three age groups showed comparable driving performance, glance durations, and number of glances. 

When driving with conditional automation (level 3):
Compared to (SAE) Level 2, drivers under Level 3 automation were less accurate in logo identification, likely due to a reduced number of glances to on-road signs. ]]></description>
      <pubDate>Thu, 23 Jul 2020 13:07:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/1723541</guid>
    </item>
    <item>
      <title>Development and Testing of an In-Vehicle Interface for Use in Automated Driving Contexts
</title>
      <link>https://rip.trb.org/View/1504789</link>
      <description><![CDATA[Despite the promise of automated vehicles and the modernization of in-vehicle interfaces, there is not a good understanding as to which design is more appropriate with respect to safety, especially as cars transition between levels of automation (e.g., between level 2 and level 1). As such, the objective of this proposed research is to develop and test an in-vehicle interface for use in automated driving contexts, with a focus on understanding the differences amongst driver groups and on delivering warning messages when the vehicle transitions between levels of automation.]]></description>
      <pubDate>Thu, 08 Mar 2018 19:23:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/1504789</guid>
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