Detection and Categorization of Altercontrol. Phase 2 Report to BMW on a Methodical Approach for the Engineering of Driver Assistance Systems

Ervin, Robert; Fancher, Paul; Hagan, Michael; Sullivan, John; Mefford, Mary Lynn; Le Blanc, David · 2001 · ROSA P / University of Michigan. Transportation Research Institute

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Summary

This paper presents the Phase II report of a study sponsored by BMW and conducted by the University of Michigan Transportation Research Institute (UMTRI), aiming to develop a methodical approach for engineering and evaluating driver assistance systems (DAS). The research addresses the challenge of designing DAS that complement human driving behavior rather than conflicting with it. To this end, the authors introduce the concept of "Altercontrol," defined as the driver’s alternative control agenda that diverges from the specific functionality of a DAS, such as Stop & Go Adaptive Cruise Control (ACC). While ACC manages longitudinal headway, Altercontrol encompasses all other driver actions motivated by factors like traffic signals, navigational tactics, safety concerns, or stylistic preferences. The study seeks to detect, categorize, and quantify these deviations to inform better system design. The methodology involved developing a model-based algorithm to detect Altercontrol in real-time during manual driving. The algorithm compares the driver’s actual longitudinal acceleration against expected actions derived from a "Headway-only" control model. This model divides the range and range-rate phase space into ten zones, each with specific expectations for driver behavior (e.g., decelerating when approaching a vehicle at high closure rates). Deviations from these expectations trigger an Altercontrol alert. The system also employs adaptive algorithms to estimate the driver’s preferred headway time ($T_h$) and open-road speed ($V_{set}$) on-the-fly, accounting for natural variability in driving style. Data were collected using a 1998 BMW 750iL instrumented with radar, vehicle sensors, and a forward-looking video camera. An experimenter accompanied the driver to annotate ambiguous events, ensuring accurate classification. Testing was conducted over approximately 400 km of mixed routes, including motorways and surface streets, under conditions likely to provoke Altercontrol, such as dense traffic. The results identified 130 distinct incidents of Altercontrol. Each incident was characterized by its occurrence zone, the driver’s control tactic, and duration. The authors developed a categorization scheme mapping these incidents into a matrix based on the type of prevailing conflict (ranging from benign discretionary controls to safety-critical actions) and the polarity of the headway-keeping error. The distribution of events across eighteen categories revealed significant patterns in how drivers deviate from strict headway control. The study also noted limitations in the initial model, particularly in Zone 4 (following), and proposed improvements to better judge Altercontrol and model driver behavior. The significance of this work lies in providing a structured framework for evaluating DAS from a human-centered perspective. By quantifying the contrast between system functionality and human preferences, the Altercontrol concept helps identify sources of customer dissatisfaction, safety risks, and usability issues. The findings suggest that cognitive modeling of these decision patterns could stimulate innovative ACC designs that are more satisfying and less risky for users. The report concludes with recommendations for refining the detection model and integrating Altercontrol observations into a broader methodical approach for DAS development, ultimately aiming to reduce development time and improve product complementarity with normal driving realities.

Key finding

The test results identified 130 distinct incidents of Altercontrol during approximately 400 km of manual driving, demonstrating that drivers frequently employ control tactics distinct from strict headway-keeping rules.

Methodology

naturalistic

Provenance

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Topics

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• steering pattern
• situational awareness
• in vehicle coaching
• automation surprise
• work zones
• following distance

Information type

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• Empirical Findings: behavioral performance data
• Theoretical Contribution: computational model, conceptual framework