Simulating Automated Emergency Braking with and without Torricelli Vacuum Emergency Braking for cyclists: Effect of brake deceleration and sensor field-of-view on accidents, injuries and fatalities

Jeppsson, Hanna; Lübbe, Nils

doi:10.1016/j.aap.2020.105538

Cited by 17 publications

(19 citation statements)

References 17 publications

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“…In the current study, the closing speed between the cyclist and the car is used, whereas Rosén (2013) chose the opponent car impact speed. Compared to another, more recent study which also used GIDAS data (Jeppsson & Lubbe, 2020), our study indicates less speed dependence (the curve is less steep), but again the differences are well within the margin of error.…”

Section: Comparison To Previous Literaturesupporting

confidence: 50%

“…Although different input data were used for training the logistic regression model (only cars with a registration year later than 1997 in Jeppsson & Lubbe (2020) and no such limitation in Rosén ( 2013)), they obtained similar results. The 10% fatality risk of cyclists corresponds to 60 km/h (Jeppsson & Lubbe, 2020) or 67 km/h (Rosén, 2013). In both studies, impact speed is given as car speed on impact, without taking bicycle speed into account.…”

Section: Literature Reviewmentioning

confidence: 79%

“…For car-to-bicycle crashes, with a cyclist impacting the front of a passenger car, Rosén (2013) and Jeppsson & Lubbe (2020) provide injury risk curves. Both studies are based on GIDAS data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In both studies, impact speed is given as car speed on impact, without taking bicycle speed into account. Even though the influence of the cyclist speed on the impact is not negligible (Spitzhüttl & Liers, 2016), Jeppsson & Lubbe (2020) found that car impact speed gives a slightly better model fit than closing speed. Rosén (2013) notes that cyclists' injury risks are lower than pedestrian risks and stresses that further research is needed to understand the differences.…”

Section: Literature Reviewmentioning

confidence: 99%

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Safe speeds: fatality and injury risks of pedestrians, cyclists, motorcyclists, and car drivers impacting the front of another passenger car as a function of closing speed and age

Lübbe

Jeppsson

2022

TSR

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As crash speed increases, so does the probability of injury. The vulnerability of different road users varies greatly, in part due to differences in their protective equipment. Therefore, for the same speed, their injury probabilities are different. The objective of this study is to define injury risk curves, mathematical relations between closing speed (the relative speed between two crash partners) and injury outcome, for different road users. These risk curves can be used to rank road user vulnerability and define safe speeds, i.e. speeds not exceeding tolerable injury probabilities. Crashes involving pedestrians, cyclists, motorcyclists, and car drivers impacting the front of another passenger car (i.e. frontal impacts from the other car’s perspective) were extracted from the German in-depth accident study (GIDAS). The injuries were modelled as a function of closing speed and road user age using a weighted binary logistic regression. In accordance with the Abbreviated Injury Scale 2015 revision, three injury severities were modelled: at-least-moderate injury severities, at-least-serious injury severities, and fatal injuries. The constructed risk curves predicted injury outcomes with an average Area under the Curve ranging from 0.66 to 0.94 in cross-validation. A 10% risk of sustaining at-least-serious injuries corresponds to a closing speed of 29 km/h for pedestrians, 44 km/h for cyclists, 48 km/h for motorcyclists, and 112 km/h for car drivers. If a 10% risk of serious injury is acceptable, the closing speeds can be translated into safe speed limits of 25 km/h for cars with pedestrian encounters; 20 to 25 km/h for cyclists, motorcyclists, and cars when they encounter each other; and 55 km/h for cars in head-on impacts. These safe speeds align with current speed limits of 20 to 30 km/h in urban centers but bring into question the current practices of much higher speed limits on rural roads shared by bicycles, motorcycles, and cars. However, safe speed limits could be increased (maintaining a 10% serious injury risk) if road users have more protective equipment and Automated Emergency Braking reliably reduces impact speeds in all crash types.

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Section: Comparison To Previous Literaturesupporting

confidence: 50%

Section: Literature Reviewmentioning

confidence: 79%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Safe speeds: fatality and injury risks of pedestrians, cyclists, motorcyclists, and car drivers impacting the front of another passenger car as a function of closing speed and age

Lübbe

Jeppsson

2022

TSR

Self Cite

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“…Appendix A shows the process of AEB intervention and the braking deceleration profile ( 33 ). AEB is triggered when the time-to-collision T TC (ratio of relative distance between vehicle and cyclist to relative speed) trigger threshold is reached.…”

Section: Methodsmentioning

confidence: 99%

Probabilistic Prediction of Collisions between Cyclists and Vehicles Based on Uncertainty of Cyclists’ Movements

Pan

Han

Jin

et al. 2022

Transportation Research Record: Journal of the Transportation R

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The uncertainty of cyclists’ movements has a significant impact on predicting the risk of collisions between cyclists and vehicles. The purpose of this study was to provide a method for assessing collision risk using probability, taking into account the uncertainty of cyclists’ movements. A cyclist model was first developed using a first-order Markov model. Then, based on Monte Carlo sampling, the distribution characteristics of the minimum distance and the time-to-collision (TTC) between the vehicle and the cyclist were extracted. By fitting these features, the probability density functions of the collision distance and TTC were estimated to derive the collision probabilities. The effectiveness of the collision probability prediction model was benchmarked against a deterministic crash risk prediction model (autonomous emergency braking [AEB] system) applied to three real-world cases previously reconstructed in an in-depth crash database. The results show that the collision probability prediction model can effectively predict the risk of collisions between cyclists and vehicles with better accuracy than AEB systems using a fixed trigger threshold. This study is a valuable reference for the development of advanced vehicle collision avoidance systems to protect cyclists and other vulnerable road users.

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ANN‐PID based automatic braking control system for small agricultural tractors

Pradhan,

Sahoo,

Kushwaha

et al. 2024

Journal of Field Robotics

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Braking system is a crucial component of tractors as it ensures safe operation and control of the vehicle. The limited space availability in the workspace of a small tractor exposes the operator to undesirable posture and a maximum level of vibration during operation. The primary cause of road accidents, particularly collisions, is attributed to the tractor operator's insufficient capacity to provide the necessary pedal power for engaging the brake pedal. During the process of engaging the brake pedal, the operator adjusts the backrest support to facilitate access to the brake pedal while operating under stressed conditions. In the present study, a linear actuator‐assisted automatic braking system was developed for the small tractors. An integrated artificial neural network proportional–integral–derivative (ANN‐PID) controller‐based algorithm was developed to control the position of the brake pedal based on the input parameters like terrain condition, obstacle distance, and forward speed of the tractor. The tractor was operated at four different speeds (i.e., 10, 15, 20, and 25 km/h) in different terrain conditions (i.e., dry compacted soil, tilled soil, and asphalt road). The performance parameters like sensor digital output (SDO), force applied on the brake pedal (), and deceleration were considered as dependent parameters. The SDO was found to good approximation for sensing the position of the brake pedal during braking. The optimized network topology of the developed multilayer perceptron neural network (MLPNN) was 3‐6‐2 for predicting SDO and deceleration of the tractor with a coefficient of determination () for the training and testing datasets of the SDO and deceleration were obtained as 0.9953 and 0.9854, and 0.9254 and 0.9096, respectively. The Ziegler–Nichols (Z‐N method) method was adopted to determine the initial optimal gains of the PID controller and later these coefficients were optimized using response surface methodology. The optimized proportional (), integral (), and derivative () coefficient values were 4.8, 6.782, and 3.15, respectively. The developed integrated ANN, that is, MLPNN and PID‐based algorithm could successfully control the position of the brake pedal during braking. The stopping distance and slip of the tractor during automatic braking increased with an increase in the forward speed for the tractor from 10 to 25 km/h in all the selected terrain conditions.

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Simulating Automated Emergency Braking with and without Torricelli Vacuum Emergency Braking for cyclists: Effect of brake deceleration and sensor field-of-view on accidents, injuries and fatalities

Cited by 17 publications

References 17 publications

Safe speeds: fatality and injury risks of pedestrians, cyclists, motorcyclists, and car drivers impacting the front of another passenger car as a function of closing speed and age

Safe speeds: fatality and injury risks of pedestrians, cyclists, motorcyclists, and car drivers impacting the front of another passenger car as a function of closing speed and age

Probabilistic Prediction of Collisions between Cyclists and Vehicles Based on Uncertainty of Cyclists’ Movements

ANN‐PID based automatic braking control system for small agricultural tractors

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