# Presentation: 082974

## SAfety VEhicles using adaptive Interface Technology Phase 1 Research ProgramQuarterly Program Review

- Task 8: Intent
- Matthew Smith
- Aug 12, 2003

## Task description

- Team Members:
- Delphi:	Matthew Smith (Lead), Harry Zhang
- Ford:	Ksenia Kozac, Jeff Greenberg
- Objective:
- Identify a list of Intents that are measurable and potentially useful for distraction mitigation and safety warning countermeasures
- Determine diagnostic measures for the reliable detection of those intents.
- Purpose:
- Increase driver acceptance of the countermeasures
- Reduce annoyance of the countermeasures
- Increase the reliability and intelligence of the countermeasures, e.g.,
- Suppress Forward Collision Warning (FCW) alert when the driver is                  accelerating toward the lead vehicle with the intent to change lanes
- Provide blind spot warning when an object is in the blind spot and                      the driver intends to change lanes
- Assume that driving-task demand is greater and more attention is required when the driver is involved in a maneuver (Distraction Mitigation)

## Deliverables and Schedule

- Deliverables:
- Task 8A: A report based on the literature review and updated task definition document
- Task 8B: A report that details the methodology, data analysis, resultant data, diagnostic measures, algorithms, and recommendations
- Schedule:
- 8A: Literature Review
- First draft of literature review is complete
- First draft currently being reviewed by team members (Delphi and Ford)
- 8B: Identify Diagnostic Measures
- ACAS FOT pilot data set has been acquired and prepared for further analysis
- Naturalistic Lane Change data set has been acquired and prepared for further analysis
- Identified how data sets will be used for this task
- Zero and first-order Markov matrices have been developed on the eye-movements during the 3 s prior to lane change
- Preliminary analysis of kinematics, controls, and lead vehicle variables during the 3 s prior to lane change

## Literature Review: Task 8a 

- Research Areas of Interest /  Literature Review Sections
- 8.1 INTRODUCTION
- 8.2 APPLICATIONS OF INTENT DETECTION
- 8.3 INTENT DETECTION
- 8.4 CONCLUSIONS
- Key Source Material
- Nuisance alert acceptance in the  ACAS FOT
- Types of FCW nuisance alerts  (CAMP FCW 1999; LeBlanc, Bareket, Ervin, & Fancher, 2002; Ervin, Sayer, & LeBlanc, 2003)
- Blind Spot Warning Activation (Tijerina and Hetrick, 1997; Mazzae and Garrot, 1995; Chovan, Tijerina, Alexander, & Hendricks, 1994)
- Lane Changes (Lee, Olsen, and Wierwille, 2002).
- Eye Movements (Mourant and Donahue, 1974; Lee, Olsen, and Wierwille, 2002)
- Prior Intent Detection Research (Liu, Veltri, and Pentland, 1998; Liu, 1999)

##  Task 8a: Literature ReviewMajor Findings: Applications

- Forward Collision Warning Nuisance Alerts (ACAS FOT pilot data)
- Lane transitions (30/92 alerts)
- Host vehicle approaching a lead vehicle changing lanes (4.3%)
- Host vehicle approach with the intent to pass (3.3%)
- Host vehicle approach with the intent to move into turning lane (2.2%)
- Lane Drift Warning (Pomerleau, et al. 1999)
- Lane-keeping performance varies greatly across individuals, driving situations, and environments  Frequent nuisance alerts
*“**A LDWS should attempt to determine driver intentions in order to minimize nuisance alarms.  It should attempt to avoid issuing warnings for intentional lane excursions which can result when performing a lane change, driving onto the shoulder to avoid obstacles in the travel lane, or stopping beside the road for a vehicle or passenger emergency.” *(p. 22)
- Blind Spot Warning
- Several researchers suggest reserving audio for events where the driver intends to change lanes (e.g., Mazzae and Garrot, 1995; Chovan et al., 1994; Young et al., 1995)
*“**A better alternative for designing lane change crash avoidance systems would be one that was keyed off a signal of the driver’s intent.  Turn signals provide this but drivers do not always use them properly.  It may be possible to discover other indicators of the driver’s intent to change lanes, if not the start of the lane change.”* (Chovan, et al., 1994, p. 36)

## Lane Change
Lee, Olsen, and Wierwille, 2002 observed 8667 naturalistic lane changes
500 lane changes cases were analyzed in depth (e.g, gaze locations, surrounding vehicles)
Turn signal used 44% of the time and varied greatly across drivers

Gaze Analysis
Carter and Laya (1998) observed that drivers spent more time glancing at the left-hand lane and rear-view mirror and less time glancing at the speedometer during overtaking compared with normal lane-keeping. 
Mourant and Donahue (1974) examined the mirror-sampling behavior of drivers during lane change maneuvers compared to baseline driving. 
Lee et al. (2002) examined eye-movements of drivers during the 3 s prior to lane changes 

Prior Intent Detection Research
Goldman et al. (1995) developed Maneuver Intent Recognition (MIR) system to supply information to a Driver-Adaptive Warning System (DAWS). 
Yuhara and Tajima (2001) applied a similar approach to provide input into their adaptive steering system.  Their intent recognition algorithm used only steering-wheel angle.
Takahashi (2000) developed intent detection system for intention to acc/decelerate
Liu (1999) used Hidden Markov Dynamic Models (HMDMs) to detect intent to change lanes 

- Lee, Olsen, and Wierwille, 2002 observed 8667 naturalistic lane changes
- 500 lane changes cases were analyzed in depth (e.g, gaze locations, surrounding vehicles)
- Turn signal used 44% of the time and varied greatly across drivers
- Gaze Analysis
- Carter and Laya (1998) observed that drivers spent more time glancing at the left-hand lane and rear-view mirror and less time glancing at the speedometer during overtaking compared with normal lane-keeping.
- Mourant and Donahue (1974) examined the mirror-sampling behavior of drivers during lane change maneuvers compared to baseline driving.
- Lee et al. (2002) examined eye-movements of drivers during the 3 s prior to lane changes
- Prior Intent Detection Research
- Goldman et al. (1995) developed Maneuver Intent Recognition (MIR) system to supply information to a Driver-Adaptive Warning System (DAWS).
- Yuhara and Tajima (2001) applied a similar approach to provide input into their adaptive steering system.  Their intent recognition algorithm used only steering-wheel angle.
- Takahashi (2000) developed intent detection system for intention to acc/decelerate
- Liu (1999) used Hidden Markov Dynamic Models (HMDMs) to detect intent to change lanes

-  Task 8a: Literature ReviewMajor Findings: Intent Detection

## Strategy for Intent Detection 
Compare Maneuver cases (I) with Non-maneuver/lane-keeping cases (N)
Examine sensitivity and bias of errors

- Compare Maneuver cases (I) with Non-maneuver/lane-keeping cases (N)
- Examine sensitivity and bias of errors

-  Task 8a: Literature ReviewSummary

## Task 8b: Research Three Available Data Sets

- 500 In-depth Lane Changes (Lee et al., 2002)
- _Advantages_:	Categorized gaze coordinates (3 s prior to maneuver)
-                                Already organized with respect to maneuvers
- _Disadvantages_:	Only contains limited types of events (e.g., no turns or braking)
- _Planned Usage_:	Lane Change/Merge/Pass (no null cases)
- Task 3 (Performance) data set
- _Advantages_:	Gaze measured with seeing machines system
- _Disadvantages_:	Only contains straight-road driving and distracted driving
- 				No forward-looking sensor except camera (must visually identify)
- _Planned Usage_:	Null cases for Lane Change/Merge/Pass
- ACAS FOT pilot data set
- Stage 3 pilot testing involved unconstrained usage of ACAS vehicle by six subjects
- _Advantages_:	Richer range of scenarios available
- _Disadvantages_:	Intermittent face video does not appear to support gaze analysis
- _Planned Usage_:	Brake maneuvers (e.g., lead vehicle and accelerator release)

## Task 8b: Research Targeted Maneuvers

- Lane Change/Merge/Pass
- 500 In-depth lane changes
- Intended maneuvers have motive
- Remaining were unintended/avoidance/other
- Brake
- ACAS FOT pilot data set
- No video data
- Avoidance Maneuver
- Ford members are currently looking into the availability of the CAMP last-second steering data set
- Turn
- No adequate data source available
- No activity planned for SAVE-IT Phase I

## Iteratively Test and Develop Algorithms
Initial deadline of August 8 not feasible due to August arrival of Task 3 data set
Estimated completion September 30
Delay not expected to impact later deliverables

Test and Refine Algorithms
Deadline of November 8th should be possible

Final Report
Deadline of January 30th should be possible

Phase II plan
Deadline of February 27th should be possible

- Initial deadline of August 8 not feasible due to August arrival of Task 3 data set
- Estimated completion September 30
- Delay not expected to impact later deliverables
- Test and Refine Algorithms
- Deadline of November 8th should be possible
- Final Report
- Deadline of January 30th should be possible
- Phase II plan
- Deadline of February 27th should be possible

- Research: Task 8b Issues/Concerns