Adaptive Cruise Control (ACC) – Objective Metrics and Performance (Post 2 of 3)

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TROY, Mich. — This is the second of three articles by Bernat Ferrer, Manager Chassis and Active Safety at Applus IDIADA U.S.

Adaptive Cruise Control (ACC) – Objective Metrics and Performance (1 of 3)

The Adaptive Cruise Control (ACC) is one of the main ADAS functionalities of the automotive industry. It automatically regulates the vehicle speed in order to keep a safe and constant longitudinal distance gap with the vehicles ahead. The development of its requirements and calibration parameters, are key to achieve not only the safety but also the comfortableness of the complete system.

This study considers many of the scenarios that an ACC system is usually challenged to develop and calibrate its main parameters: deceleration with decreasing target speed, target vehicle cut-out, approaching a slower target vehicle, etc. In order to characterize these events, some metrics have been defined, willing to objectivize the vehicle and system performance: longitudinal braking deceleration, acceleration overshoots, jerk, vehicle reaction delays, etc. Signals coming from vehicles instrumented with sensors and tested at IDIADA proving ground tracks, have been acquired for the corresponding analysis. Different vehicle segments and systems were assessed to generate a complete user profile study.

The first part of this article introduced the problem and explained the methodology behind the subjective assessment, while this second part describes the test scenarios and focuses on the objective analysis.

  • Methodology
  • Test scenarios

Once the subjective assessment has been depicted, some test protocols have been defined, in order to summarize and encompass the braking actuation that a driver can perceive from the ACC performance. The scenarios considered include pure car-to-car longitudinal braking tests, obtained from:

  • Approaching with slower target vehicle.
  • Approaching with target vehicle braking: different deceleration levels (from 1 m/s2 to 4 m/s2).
  • Cut-in: target vehicle driving in another lane and cutting in the test vehicle trajectory, provoking its brake actuation.

Different test speeds have been also set, in order to cover an extened range of driving conditions of the vehicle while using the ACC system. The main ranges considered for this study were the ones compressed between 170 km/h and 50 km/h, with considerable high deltas of speed between vehicles.

Moreover, all the objective tests were carried out at IDIADA Proving Ground test tracks, keeping always a robust and careful performance of the scenarios defined.

  • Objective analysis

Keeping with the objectivization process, not only the test scenarios are important to produce the necessary outputs, but also the data generation and sensor singals obtention. This methodology will try to get as much accuracy as possible in terms of concepts similarity; in other words, to apply the theory to the data post-processing in order to reproduce as much as possible the driver’s own perception. This will be key for the obtention of coherent results when comparing (and trusting) both subjective and objective fields.

In this case, for the generation of the data recorded during the defined test scenarios, some sensors and equipment have been installed on the vehicles under test. Focusing on the braking actuation, the following signals were gathered:

  • Time vs. Longitudinal acceleration
  • Time vs. Jerk (obtained from the post-treatment of the longitudinal acceleration)

As a final step of this methodology, an analysis of these signals under the mentioned test scenarios has been implemented. This part is not only important to define which signals are post-processed and how (filters, offsets, etc.) but mainly to generate the necessary metrics that can match better with the subjective assessment.

That is why, a similar approach as in the first point of this section has been followed, by splitting-up the braking scenarios in three steps. For each one of them, some metrics have been considered, in order to objectivize the reaction perceived by the driver during the ACC system actuation.

  1. Braking beginning:

In order to match with the subjective parameters defined, two metrics were selected: average jerk and ramp-up linearity error. The first reflects the resulting slope of the whole deceleration increase ramp, by averaging the jerk of that braking step. The second one, quantifies the difference between the real deceleration ramp, compared to the theoretical and linearly perfect one, providing information on the oscillation or rebounds felt during this phase.

Adaptive Cruise
Figure 2: Deceleration post-process – Braking beginning

2. Braking quality:

The second step of the brake application, focused on the “steady-state” of the actuation, is also characterized in this evaluation with the following selection of mectrics: absolute deceleration level achieved, meaning the average deceleration reached; deceleration continuity, which considers the error accumulated between the real acceleration oscillation obtained and the theoretical perfect line; relative deceleration difference, meaning the delta of the decelerations with respect to the front vehicle, and relative to this front target vehicle deceleration.

Figure 3: Deceleration post-process – Steady braking

3. Braking ending:

The last part refers to the end of braking, which means the characterization of the braking torque and deceleration ramp-down (release). In this case, the metrics defined would also consider the distancing between the real deceleration curve and its linear approximation, the slope of the ramp-down (average jerk) and the duration of this final phase between the start of the braking release and the complete end of braking.

Figure 4: Deceleration post-process – Braking ending

All in all, an association matrix can be generated, by matching the objective metrics defined with its corresponding subjective concept.

The following table summarizes this process:

Table 2: Subjective evaluation scale

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With more than 25 years’ experience and 2,450 engineers specializing in vehicle development, Applus IDIADA is a leading engineering company providing design, testing, engineering, and homologation services to the automotive industry worldwide.

Applus IDIADA is in California and Michigan, with further presence in 25 other countries, mainly in Europe and Asia.

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