Rear-end collisions account for almost 30% of automotive crashes. Rear-end collision avoidance systems (RECASs) may offer a promising approach to help drivers avoid these crashes. Two experiments performed using a high-fidelity motion-based driving simulator examined driver responses to evaluate the efficacy of a RECAS. The first experiment showed that early warnings helped distracted drivers react more quickly--and thereby avoid more collisions--than did late warnings or no warnings. Compared with the no-warning condition, an early RECAS warning reduced the number of collisions by 80.7%. Assuming collision severity is proportional to kinetic energy, the early warning reduced collision severity by 96.5%. In contrast, the late warning reduced collisions by 50.0% and the corresponding severity by 87.5%. The second experiment showed that RECAS benefits even undistracted drivers. Analysis of the braking process showed that warnings provide a potential safety benefit by reducing the time required for drivers to release the accelerator. Warnings do not, however, speed application of the brake, increase maximum deceleration, or affect mean deceleration. These results provide the basis for a computational model of driver performance that was used to extrapolate the findings and identify the most promising parameter settings. Potential applications of these results include methods for evaluating collision warning systems, algorithm design guidance, and driver performance model input.

https://journals.sagepub.com/doi/pdf/10.1518/0018720024497844

Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator.

In 1995, NHTSA began employing the Crashworthiness Data System (CDS) to obtain more in-depth information on driver inattention-related crash causes, including drowsiness and many forms of distraction. CDS is potentially an important source of information of this issue because it is broadly representative of U.S. passenger vehicle towaway crashes and because its investigations are moderately in-depth. This research paper reports the results of the 1995 CDS data collection on this issue. The three major forms of driver inattention and their percent involvement in 1995 CDS crashes are: distraction (13.2%), looked but did not see (9.7%), and sleepy/fell asleep (2.6%). Findings from this CDS data collection have both similarities to, and differences from, previous research on the role of driver inattention in crashes. Keywords: Driver distraction

/pdf/the-role-of-driver-inattention-in-crashes-new-statistics-11nsg3v00p.pdf

The role of driver inattention in crashes: new statistics from the 1995 crashworthiness data system

Vehicle Safety Communications Project Task 3 Final Report Identify Intelligent Vehicle Safety Applications Enabled by DSRC

Objective: This study examined the effectiveness of rear-end collision warnings presented in different sensory modalities as a function of warning timing in a driving simulator. Background: The proliferation of in-vehicle information and entertainment systems threatens driver attention and may increase the risk of rear-end collisions. Collision warning systems have been shown to improve inattentive and/or distracted driver response time (RT) in rear-end collision situations. However, most previous rear-end collision warning research has not directly compared auditory, visual, and tactile warnings. Method: Sixteen participants in a fixed-base driving simulator experienced four warning conditions: no warning, visual, auditory, and tactile. The warnings activated when the time-to-collision (TTC) reached a critical threshold of 3.0 or 5.0 s. Driver RT was captured from a warning below critical threshold to brake initiation. Results: Drivers with a tactile warning had the shortest mean RT. Drivers with a tactile warning had significantly shorter RT than drivers without a warning and had a significant advantage over drivers with visual warnings. Conclusion: Tactile warnings show promise as effective rear-end collision warnings. Application: The results of this study can be applied to the future design and evaluation of automotive warnings designed to reduce rear-end collisions.

A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving

http://www.stantonltd.co.uk/shopimages/Human%20Error%20Safety%20Science%20Stanton%20&%20Salmon.pdf

Human error taxonomies applied to driving: A generic driver error taxonomy and its implications for intelligent transport systems

This report describes an analysis of the application of Intelligent Vehicle Highway System (IVHS) technology to the reduction of rear-end crashes. The principal countermeasure concept examined is a headway detection (HD) system that would detect stopped or slower-moving vehicles in a vehicle's travel path. The report is organized to correspond to the major steps of the project methodology: (1) Quantify baseline target crash problem size and describe target crash characteristics; (2) Describe, analyze, and model target crash scenarios to permit understanding of principal crash causes, time and motion sequences, and potential interventions; (3) Assess countermeasure technology and mechanisms of action to identify candidate solutions; (4) Assess relevant human factors and other (e.g., environmental, vehicle) factors affecting crash scenario and potential countermeasure effectiveness; (5) Model countermeasure action to predict effectiveness and identify critical countermeasure functional requirements; and (6) Identify specific priority technological, human factors, and other R&D issues to be resolved. Case reconstructions and modeling indicate that most rear-end crashes are due to driver inattention, and that this inattention can in theory be addressed successfully by the HD countermeasure concept and available radar technologies.

Assessment of IVHS countermeasures for collision avoidance : rear-end crashes

This report synthesizes the results of a preliminary analysis of nine major target crashes: (1) rear-end, (2) backing, (3) lane change and merge, (4) single vehicle roadway departure, (5) opposite direction, (6) signalized intersection, straight crossing path, (7) unsignalized intersection, straight crossing path, (8) left turn across path, and (9) reduced visibility. This report provides statistical descriptions of target crash sizes and characteristics, identifies crash subtypes and causal factors, defines Intelligent Transportation System (ITS) Collision Avoidance System (CAS) concepts, and includes a sample of kinematic models representing crash avoidance actions. A case-by-case examination of a sample of 1,183 crashes identified 18 crash subtypes and showed that driver recognition and driver decision errors were the primary causes of 44% and 23% of target crashes, respectively. The CAS concepts discussed in this report provide mechanisms of intervention in three basic categories: advisory, warning, and automatic control intervention. Crash avoidance actions are kinematically modeled as applied to target crash subtypes in terms of braking, steering, and holding course actions. This report concludes by highlighting key results of the analysis.

Synthesis report: examination of target vehicular crashes and potential its countermeasures. final report

This report synthesizes the results of a preliminary analysis of nine major target crashes: (1) rear-end, (2) backing, (3) lane change and merge, (4) single vehicle roadway departure, (5) opposite direction, (6) signalized intersection, straight crossing path, (7) unsignalized intersection, straight crossing path, (8) left turn across path, and (9) reduced visibility This report provides statistical descriptions of target crash sizes and characteristics, identifies crash subtypes and causal factors, defines intelligent transportation system (ITS) collision avoidance system (CAS) concepts, and includes a sample of kinematic models representing crash avoidance actions A case-by-case examination of a sample of 1,183 crashes identified 18 crash subtypes and showed that driver recognition and driver decision errors were the primary causes of 44% and 23% of target crashes, respectively The CAS concepts discussed in this report provide mechanisms of intervention in three basic categories: advisory, warning, and automatic control intervention Crash avoidance actions are kinematically modeled as applied to target crash subtypes in terms of braking, steering, and holding course actions This report concludes by highlighting key results of the analysis

M Mironer

Papers

Assessment of IVHS countermeasures for collision avoidance : rear-end crashes

Synthesis report: examination of target vehicular crashes and potential its countermeasures. final report

Synthesis report: examination of target vehicular crashes and potential ITS countermeasures