Estimating Relevance for the Emergency Electronic Brake Light Application

Abstract

In this paper, we compare two methods of estimating relevance for the emergency electronic brake light application: One method uses an analytically derived formula based on the minimum safety gap that is required to avoid a collision, whereas the other method uses a machine learning approach. The application works by disseminating reports about vehicles that perform emergency deceleration in an effort to warn drivers about the need to perform emergency braking. Vehicles that receive such reports have to decide on whether the information contained in the report is relevant to the driver and warn the driver if that is the case. Common ways of determining relevance are based on the lane or direction information, but using only these attributes can lead to many false warnings, which can desensitize the driver. Desensitized drivers may ignore warnings or completely turn off the system, thus eliminating any safety benefits of the application. We show that the machine learning method, compared with the analytically derived formula, can significantly reduce the number of false warnings by learning from the actions that drivers take after receiving a report. The methods were compared using simulated experiments with a range of traffic and communication parameters.

Efficiently Bounding the Probabilities of Vehicle Collision at Intelligent Intersections

“…Emergency braking in C-ITS is addressed in [16], where the relevance of the content of emergency braking messages for the recipient vehicle is evaluated using a machine learning approach. In [17] a path prediction algorithm for vehicles is proposed for use in an emergency braking scenario.…”

Performance Measurement Evaluation Framework and Co-Benefit\/Tradeoff Analysis for Connected and Automated Vehicles (CAV) Applications: A Survey

“…The Forward Collision Warning application is a relatively mature application, which is commonly used to improve situation awareness and enhance safety performance. The effectiveness among several pre-collision system algorithms was examined using Time-to-Collision (TTC) as a surrogate collision risk evaluation in [19], where Kusano Brake Light application-related algorithm, and showed safety benefits represented by the lower average number of collisions [20]. However, besides potential safety benefits, potential mobility and environmental impacts gains/ costs still remain to be shown in both [19] and [20], where safety benefits are probably achieved at the expense of larger greenhouse gas (GHG) emissions due to increased stop-and-go behavior.…”

“…The effectiveness among several pre-collision system algorithms was examined using Time-to-Collision (TTC) as a surrogate collision risk evaluation in [19], where Kusano Brake Light application-related algorithm, and showed safety benefits represented by the lower average number of collisions [20]. However, besides potential safety benefits, potential mobility and environmental impacts gains/ costs still remain to be shown in both [19] and [20], where safety benefits are probably achieved at the expense of larger greenhouse gas (GHG) emissions due to increased stop-and-go behavior. This might happen in other similar safety-oriented collision avoidance applications, e.g., intersection collision warnings, curve speed warnings and [38] An eco-speed harmonization scheme using V2I and I2V to smooth the individual vehicle's speed profile and to reduce the overall energy consumption AERIS [39] An eco-approach departure application which utilizes SPaT and preceding vehicles information to guide drivers to pass through intersections smoothly AUTOPIA [31] An automated on-ramp merging system which consists of the distance reference system and a fuzzy control on vehicle's longitudinal control to improve traffic flow and congestion in a merging situation…”

Cooperative emergency braking warning system in vehicular networks

“…Based on previous research, protocol standards and field trial works, the US Department of Transportation has published information announcing a decision to move forward with vehicle-to-vehicle communication technology for light vehicles in 2014 [6]. The typical safety application in the vehicular networks is the emergency electronic brake light (EEBL) [7], as shown in Figure 1. The second car may stop without collision when the first car brakes hard, based on driver vision, but the third car cannot stop fast enough and collides with the second car.…”