Giovanni Savino

Decision logic of an active braking system for powered two wheelers

Powered two-wheeler (PTW) users are exposed to a high risk of accidents leading to severe injuries and fatalities. The trend of PTW accidents has pointed out the need for an intervention on PTW safety with new and effective solutions. One of the possible answers came from the EC-funded Powered two wheeler Integrated Safety (PISa) project which identified the autonomous braking of the vehicle as one of the most promising safety functions for PTWs. The aim of this paper is to report on the design of the decision logic for deploying a PTW autonomous braking system in case of an imminent collision. Rationales and limitations for this pioneering application are given. The feasibility of the autonomous deceleration is demonstrated by an experimental study conducted with the PISa test bike implementing a prototype of the autonomous braking system, named the Active Braking (AB) system.

Evaluation of an Autonomous Braking System in Real-World PTW Crashes

Objectives: Powered 2-wheelers (PTWs) are becoming increasingly popular in Europe. They have the ability to get around traffic queues, thus lowering fuel consumption and increasing mobility. The risk of rider injury in a traffic crash is however much higher than that associated with car users. The European project, Powered Two Wheeler Integrated Safety (PISa), identified an autonomous braking system (AB) as a priority to reduce the injury consequences of a PTW crash. The aim of this study was to assess the potential effectiveness of the AB system developed in PISa, taking into account the specific system characteristics that emerged during the design, development and testing phases. Methods: Fifty-eight PTW cases representing European crash configurations were examined, in which 43 percent of riders sustained a Maximum Abbreviated Injury Scale (MAIS) 2+ injury. Two of the most common crash types were a PTW impacting a stationary object (car following scenario) 16% and an object pulling across the PTW path (crossing scenario) 54%. An expert team analysed the in-depth material of the sample crashes and determined a posteriori to what extent the AB would have affected the crash. For those cases where the AB was evaluated as applicable, a further quantitative evaluation of the benefits was conducted by considering a set of different possible rider reactions in addition to that exhibited in the actual crash. Results: In 67 percent of cases, the application of AB could have mitigated the crash outcome. Analysis of those real crash cases showed the potential for an expert rider to avoid the collision. An early reaction of the rider, associated with a correct application of the brakes would have avoided 18 of the 37 car following/crossing scenarios. Conversely, according to the analysis, an expert rider would not have been able to avoid 19 of the 37 cases. In 14 of those 19 cases, the AB would have contributed to mitigating the crash outcome. Conclusions: This study demonstrated significant potential for application of the autonomous braking system in car following and crossing scenarios. In addition, the theoretical benefit curves for the AB globally, were able to provide good quantitative indications of its benefits in real cases where the AB was considered applicable. Further analysis with larger databases is suggested in order to confirm the magnitude of benefits in the PTW crash population. Supplemental materials are available for this article. Go to the publishers online edition of Traffic Injury Prevention to view the supplemental file.

Analysis of the minimum swerving distance for the development of a motorcycle autonomous braking system

In the recent years the autonomous emergency brake (AEB) was introduced in the automotive field to mitigate the injury severity in case of unavoidable collisions. A crucial element for the activation of the AEB is to establish when the obstacle is no longer avoidable by lateral evasive maneuvers (swerving). In the present paper a model to compute the minimum swerving distance needed by a powered two-wheeler (PTW) to avoid the collision against a fixed obstacle, named last-second swerving model (Lsw), is proposed. The effectiveness of the model was investigated by an experimental campaign involving 12 volunteers riding a scooter equipped with a prototype autonomous emergency braking, named motorcycle autonomous emergency braking system (MAEB). The tests showed the performance of the model in evasive trajectory computation for different riding styles and fixed obstacles.

Reconsidering the Safety in Numbers Effect for Vulnerable Road Users: An Application of Agent-Based Modeling

Objective: Increasing levels of active transport provide benefits in relation to chronic disease and emissions reduction but may be associated with an increased risk of road trauma. The safety in numbers (SiN) effect is often regarded as a solution to this issue; however, the mechanisms underlying its influence are largely unknown. We aimed to (1) replicate the SiN effect within a simple, simulated environment and (2) vary bicycle density within the environment to better understand the circumstances under which SiN applies. Methods: Using an agent-based modeling approach, we constructed a virtual transport system that increased the number of bicycles from 9% to 35% of total vehicles over a period of 1,000 time units while holding the number of cars in the system constant. We then repeated this experiment under conditions of progressively decreasing bicycle density. Results: We demonstrated that the SiN effect can be reproduced in a virtual environment, closely approximating the exponential relationships between cycling numbers and the relative risk of collision as shown in observational studies. The association, however, was highly contingent upon bicycle density. The relative risk of collisions between cars and bicycles with increasing bicycle numbers showed an association that is progressively linear at decreasing levels of density. Conclusions: Agent-based modeling may provide a useful tool for understanding the mechanisms underpinning the relationships previously observed between volume and risk under the assumptions of SiN. The SiN effect may apply only under circumstances in which bicycle density also increases over time. Additional mechanisms underpinning the SiN effect, independent of behavioral adjustment by drivers, are explored.

Further Development of Motorcycle Autonomous Emergency Braking (MAEB), What Can In-Depth Studies Tell Us? A Multinational Study

Objective: In 2006, Motorcycle Autonomous Emergency Braking (MAEB) was developed by a European Consortium (Powered Two Wheeler Integrated Safety, PISa) as a crash severity countermeasure for riders. This system can detect an obstacle through sensors in the front of the motorcycle and brakes automatically to achieve a 0.3 g deceleration if the collision is inevitable and the rider does not react. However, if the rider does brake, full braking force is applied automatically. Previous research into the potential benefits of MAEB has shown encouraging results. However, this was based on MAEB triggering algorithms designed for motorcycle crashes involving impacts with fixed objects and rear-end crashes. To estimate the full potential benefit of MAEB, there is a need to understand the full spectrum of motorcycle crashes and further develop triggering algorithms that apply to a wider spectrum of crash scenarios. Methods: In-depth crash data from 3 different countries were used: 80 hospital admittance cases collected during 2012–2013 within a 3-h driving range of Sydney, Australia, 40 crashes with Injury Severity Score (ISS) > 15 collected in the metropolitan area of Florence, Italy, during 2009–2012, and 92 fatal crashes that occurred in Sweden during 2008–2009. In the first step, the potential applicability of MAEB among the crashes was assessed using a decision tree method. To achieve this, a new triggering algorithm for MAEB was developed to address crossing scenarios as well as crashes involving stationary objects. In the second step, the potential benefit of MAEB across the applicable crashes was examined by using numerical computer simulations. Each crash was reconstructed twice—once with and once without MAEB deployed. Results: The principal finding is that using the new triggering algorithm, MAEB is seen to apply to a broad range of multivehicle motorcycle crashes. Crash mitigation was achieved through reductions in impact speed of up to approximately 10 percent, depending on the crash scenario and the initial vehicle pre-impact speeds. Conclusions: This research is the first attempt to evaluate MAEB with simulations on a broad range of crash scenarios using in-depth data. The results give further insights into the feasibility of MAEB in different speed ranges. It is clear then that MAEB is a promising technology that warrants further attention by researchers, manufacturers, and regulators.

Inevitable Collision States for Motorcycle-to-Car Collision Scenarios

This paper presents a method to identify inevitable collision states (ICS) specifically for a motorcycle when interacting with an opponent passenger car in typical traffic scenarios. Previous ICS methods were applied to passenger cars or generic vehicles; however, the peculiarities of motorcycles urge the definition of specific methods for these vehicles. The findings extend the applicability of previous algorithms to include all motorcycle-to-car collisions, irrespective of collision configurations. ICS identification can be adopted as a triggering criterion for more invasive safety systems such as motorcycle autonomous emergency braking (MAEB), which require a last-resort approach in their initial phases of development. In this regard, this paper also presents an evaluation of an idealized MAEB through experiments simulating real-world crashes in a computer-based virtual environment.

Real-time estimation of road–tyre adherence for motorcycles

Improving braking skills of a rider supported by a real-time training device embedded in the motorcycle represents a possible strategy to deal with safety issues associated with the use of powered two wheelers. A challenging aspect of the braking trainer system is the evaluation of the adherence between tyre and road surface on each wheel. This paper presents a possible method to evaluate the current and maximum adherence during a braking manoeuvre. The proposed approach was positively validated through multi-body simulations and experimental data acquired in naturalistic riding conditions.

Exploratory field trial of motorcycle autonomous emergency braking (MAEB): Considerations on the acceptability of unexpected automatic decelerations

ABSTRACT Objective: Autonomous emergency braking (AEB) acts to slow down a vehicle when an unavoidable impending collision is detected. In addition to documented benefits when applied to passenger cars, AEB has also shown potential when applied to motorcycles (MAEB). However, the feasibility of MAEB as practically applied to motorcycles in the real world is not well understood. Methods: In this study we performed a field trial involving 16 riders on a test motorcycle subjected to automatic decelerations, thus simulating MAEB activation. The tests were conducted along a rectilinear path at nominal speed of 40 km/h and with mean deceleration of 0.15 g (15% of full braking) deployed at random times. Riders were also exposed to one final undeclared brake activation with the aim of providing genuinely unexpected automatic braking events. Results: Participants were consistently able to manage automatic decelerations of the vehicle with minor to moderate effort. Results of undeclared activations were consistent with those of standard runs. Conclusions: This study demonstrated the feasibility of a moderate automatic deceleration in a scenario of motorcycle travelling in a straight path, supporting the notion that the application of AEB on motorcycles is practicable. Furthermore, the proposed field trial can be used as a reference for future regulation or consumer tests in order to address safety and acceptability of unexpected automatic decelerations on a motorcycle.

Triggering algorithm based on inevitable collision states for autonomous emergency braking (AEB) in motorcycle-to-car crashes

This study presents a triggering algorithm for a collaborative, motorcycle-to-car collision avoidance system that slows down the car without input of the driver when the collision becomes imminent. The algorithm is based on the concept of inevitable state collisions. Example applications of the proposed algorithm were obtained via 2D computer simulations representing a data set of real crashes occurred in Italy, Sweden and Australia. Results indicated that the proposed method can apply to typical crash scenarios.

A robust estimation of the effects of motorcycle autonomous emergency braking (MAEB) based on in-depth crashes in Australia

ABSTRACT Objective: Autonomous emergency braking (AEB) is a safety system that detects imminent forward collisions and reacts by slowing down the host vehicle without any action from the driver. AEB effectiveness in avoiding and mitigating real-world crashes has recently been demonstrated. Research suggests that a translation of AEB to powered 2-wheelers could also be beneficial. Previous studies have estimated the effects of a motorcycle AEB system (MAEB) via computer simulations. Though effects of MAEB were computed for motorcycle crashes derived from in-depth crash investigation, there may be some inaccuracies due to limitations of postcrash investigation (e.g., inaccuracies in preimpact velocity of the motorcycle). Furthermore, ideal MAEB technology was assumed, which may lead to overestimation of the benefits. This study sought to evaluate the sensitivity of the simulations to variations in reconstructed crash cases and the capacity of the MAEB system in order to provide a more robust estimation of MAEB effects. Methods: First, a comprehensive classification of accidents was used to identify scenarios in which MAEB was likely to apply, and representative crash cases from those available for this study were populated for each crash scenario. Second, 100 variant cases were generated by randomly varying a set of simulation parameters with given normal distributions around the baseline values. Variants reflected uncertainties in the original data. Third, the effects of MAEB were estimated in terms of the difference in the impact speed of the host motorcycle with and without the system via computer simulations of each variant case. Simulations were repeated assuming both an idealized and a realistic MAEB system. For each crash case, the results in the baseline case and in the variants were compared. A total of 36 crash cases representing 11 common crash scenarios were selected from 3 Australian in-depth data sets: 12 cases from New South Wales, 13 cases from Victoria, and 11 cases from South Australia. Results: The reduction in impact speed elicited by MAEB in the baseline cases ranged from 2.8 to 10.0 km/h. The baseline cases over- or underestimated the mean impact speed reduction of the variant cases by up to 20%. Constraints imposed by simulating more realistic capabilities for an MAEB system produced a decrease in the estimated impact speed reduction of up to 14% (mean 5%) compared to an idealized system. Conclusions: The small difference between the baseline and variant case results demonstrates that the potential effects of MAEB computed from the cases described in in-depth crash reports are typically a good approximation, despite limitations of postcrash investigation. Furthermore, given that MAEB intervenes very close to the point of impact, limitations of the currently available technologies were not found to have a dramatic influence on the effects of the system.