Autonomous emergency braking systems adapted to snowy road conditions improve drivers' perceived safety and trust

Adaptive autonomous emergency braking model based on weather conditions

OBJECTIVE

Vehicle active safety systems can improve vehicle security by avoiding collisions. An autonomous emergency braking (AEB) system's safety distance calculation is usually based on normal weather conditions. The AEB system's early warning capabilities decrease during adverse weather conditions.

METHODS

A multilayer perceptron (MLP) model is used to obtain data from accident and weather data sets. The MLP model is trained and the severity of accidents is predicted. The severity is used as a parameter to build an adaptive AEB system algorithm that considers adverse weather conditions.

RESULTS

The adaptive AEB system algorithm increases safety and reliability under adverse weather conditions. Prescan and a driver-in-the-loop system are used to test the adaptive AEB model. Both tests show that the adaptive AEB model has better performance under adverse weather than the traditional AEB model.

CONCLUSIONS

The experimental results demonstrate that the adaptive AEB system can increase the safety distance in rainy weather and avoid collisions under hazy conditions.

Analysis of the Impact of Invisible Road Icing on Selected Parameters of a Minibus Vehicle

The measurement of acceleration during vehicle motion can be used to assess the driving styles and behaviours of drivers, to control vehicle traffic, to detect uncontrolled vehicle behaviour, and to prevent accidents. The measurement of acceleration during vehicle motion on an icy road can be used to warn the driver about changing conditions and the related hazards. This paper presents the results of testing the motion parameters of a Ford Transit adapted for passenger transport in critical traffic conditions. It can contribute to the improvement of road safety. Critical traffic conditions are deemed in the paper as sudden braking, rapid acceleration, and circular vehicle motion at maximum speed maintainable in the given conditions. The vehicle's acceleration and speed were measured during the tests. The tests were carried out with a TAA linear acceleration sensor and a Correvit S-350 Aqua optoelectronic sensor. The same test runs were conducted on a dry surface, a wet (after rain) surface and a surface covered with a thin, invisible ice layer. The objective of the tests was to determine the impact of invisible road icing, the so-called black ice, on the tested vehicle's braking, acceleration, and circular motion. It was demonstrated that a virtually invisible ice layer covering the road surface has a substantial impact on the tested vehicle's motion parameters, thereby affecting traffic safety. It substantially extends the braking and acceleration distances and requires the driver to reduce the vehicle's speed when performing circular motions. A clear wet surface, representing motion after rain, did not substantially affect the analysed parameters. The obtained results can be used in traffic simulations and to analyse the causes of accidents.

Evaluation of automated emergency braking system's avoidance of pedestrian crashes at intersections under occluded conditions within a virtual simulator

Pedestrians' red-light crossing can present a threat to themselves and the safety at intersections, especially for the through vehicles since their speeds are higher compared to the turning vehicles. The automated emergency braking (AEB) system could actively detect pedestrians and react to avoid potential conflicts. This study contributes to evaluate the effectiveness of the AEB system under occlusion conditions. The braking algorithm was developed in the virtual simulator CARLA to control the ego vehicle. Three occlusion scenarios in which the sensor of the AEB system could not detect the pedestrian if the pedestrian is occluded by a stopping vehicle. The evaluation experiments were conducted at a typical 4-leg intersection considering different motion statuses of the ego vehicle and pedestrian. The effects of field of view (FOV) of the sensor and activation threshold of the AEB system were also explored. The study indicated that the effectiveness of the AEB system could be reduced by the occlusion time. A longer activation threshold is recommended if the pedestrian is potentially occluded for a long time. The effects of other factors such as the speed of the ego vehicle and pedestrian and scenarios were also identified.

Effects of Automation and Fatigue on Drivers from Various Age Groups

This study explores how drivers are affected by automation when driving in rested and fatigued conditions. Eighty-nine drivers (45 females, 44 males) aged between 20 and 85 years attended driving experiments on separate days, once in a rested and once in a fatigued condition, in a counterbalanced order. The results show an overall effect of automation to significantly reduce drivers’ workload and effort. The automation had different effects, depending on the drivers’ conditions. Differences between the manual and automated mode were larger for the perceived time pressure and effort in the fatigued condition as compared to the rested condition. Frustration was higher during manual driving when fatigued, but also higher during automated driving when rested. Subjective fatigue and the percentage of eye closure (PERCLOS) were higher in the automated mode compared to manual driving mode. PERCLOS differences between the automated and manual mode were higher in the fatigued condition than in the rested condition. There was a significant interaction effect of age and automation on drivers’ PERCLOS. These results are important for the development of driver-centered automation because they show different benefits for drivers of different ages, depending on their condition (fatigued or rested).

Occupant safety effectiveness of proactive safety seat in autonomous emergency braking

The proactive safety seat (PSS) is a recently developed active safety system for securing occupant safety in out-of-seat position (OOSP), which was applied in the Hyundai Genesis G80 in 2020. However, there has not been sufficient quantifiable verification supporting the effectiveness of the PSS. The present study was performed to determine the effectiveness of the PSS for occupant safety in OOSP and to identify areas for additional improvement. Six test conditions were considered to determine the effectiveness of the PSS for augmentation of occupant safety in OOSP. Ten healthy men participated in the tests. Compared with the no PSS condition, maximum head excursion and neck rotation were significantly decreased in the PSS condition by 0.6-0.8-fold and 0.6-0.7-fold, respectively (P < 0.05). The PSS condition in which the seat pan was moved forward to the mid position showed a greater effect in reducing the characteristic motions related to submarining, compared with the condition in which the seat pan was moved rearward to the mid position (P < 0.05). These results suggested that PSS augments occupant safety in OOSP. This study provides valuable insights in ameliorating risks to the occupant in unintended seat positions before braking and/or collision.

Influence of the feedback links of connected and automated vehicle on rear-end collision risks with vehicle-to-vehicle communication

OBJECTIVE

Connected and automated vehicles (CAV) can monitor multiple vehicles ahead via vehicle-to-vehicle (V2V) communication. Although feedback information from more vehicles ahead may be more helpful for anticipations, it also makes control more complex and increases the probability of data packet loss. Then it needs an appropriate number of CAV feedback links, and the maximum number may be not suitable. Therefore, this article focuses on the influence of CAV feedback links on rear-end collision risks.

METHODS

To deal with this, stability analysis of a CAV car-following model was conducted to obtain the designs of CAV feedback gains for maintaining stable CAV flow. Simulation experiments were performed to describe a traffic accident on freeway, using car-following models of manually driven vehicles (MDVs) and CAV under different CAV penetration rates. Four scenarios are considered in simulation experiments; that is, the CAV monitors 1, 2, 3, and 4 preceding vehicles, respectively. Based on the simulation experiments, surrogate safety indicators, time-exposed time-to-collision (TET), and time-integrated time-to-collision (TIT) are used to evaluate risks of rear-end collisions.

RESULTS

Results indicated that CAV helped to decrease the collision risks, especially the more serious collision risks with smaller threshold values of time-to-collision (TTC). In addition, the reductions in collision risks are more obvious when CAV feedback changes from one link to 2 links. In addition, reducing amplitudes are not significant if the CAV feedback is extended from 2 links to 3 or 4 links.

CONCLUSIONS

Two links of CAV feedback are appropriate when control complexity is a priority, whereas 3 links is the better choice when reductions in collisions are a priority. The findings of this study provide helpful reference for CAV control and design before larger-scale implementation in real vehicles.

Characteristics of rear-end crashes involving passenger vehicles with automatic emergency braking

Objectives: Automatic emergency braking (AEB) is a proven effective countermeasure for preventing front-to-rear crashes, but it has not yet fully lived up to its estimated potential. This study identified the types of rear-end crashes in which striking vehicles with AEB are overrepresented to determine whether the system is more effective in some situations than in others, so that additional opportunities for increasing AEB effectiveness might be explored. Methods: Rear-end crash involvements were extracted from 23 U.S. states during 2009-2016 for striking passenger vehicles with and without AEB among models where the system was optional. Logistic regression was used to examine the odds that rear-end crashes with various characteristics involved a striking vehicle with AEB, controlling for driver and vehicle features. Results: Striking vehicles were significantly more likely to have AEB in crashes where the striking vehicle was turning relative to when it was moving straight (odds ratio [OR] = 2.35; 95% confidence interval [CI], 1.76, 3.13); when the struck vehicle was turning (OR = 1.66; 95% CI, 1.25, 2.21) or changing lanes (OR = 2.05; 95% CI, 1.13, 3.72) relative to when it was slowing or stopped; when the struck vehicle was not a passenger vehicle or was a special use vehicle relative to a car (OR = 1.61; 95% CI, 1.01, 2.55); on snowy or icy roads relative to dry roads (OR = 1.83; 95% CI, 1.16, 2.86); or on roads with speed limits of 70+ mph relative to those with 40 to 45 mph speed limits (OR = 1.49; 95% CI, 1.10, 2.03). Overall, 25.3% of crashes where the striking vehicle had AEB had at least one of these overrepresented characteristics, compared with 15.9% of strikes by vehicles without AEB. Conclusions: The typical rear-end crash occurs when 2 passenger vehicles are proceeding in line, on a dry road, and at lower speeds. Because atypical crash circumstances are overrepresented among rear-end crashes by striking vehicles with AEB, it appears that the system is doing a better job of preventing the more typical crash scenario. Consumer information testing programs of AEB use a test configuration that models the typical rear-end crash type. Testing programs promoting good AEB performance in crash circumstances where vehicles with AEB are overrepresented could guide future development of AEB systems that perform well in these additional rear-end collision scenarios.