Young drivers’ pedestrian anti-collision braking operation data modelling for ADAS development

Smart cities and smart mobility come from intelligent systems designed by humans. Artificial Intelligence (AI) is contributing significantly to the development of these systems, and the automotive industry is the most prominent example of "smart" technology entering the market: there are Advanced Driver Assistance System (ADAS), Radar/LIDAR detection units and camera-based Computer Vision systems that can assess driving conditions. Actually, these technologies have become consumer goods and services in mass-produced vehicles to provide human drivers with tools for a more comfortable and safer driving. Nevertheless, they need to be further improved for progress in the transition to fully automated driving or simply to increase vehicle automation levels. To this end, it becomes imperative to accurately predict driver’s decisions, model human driving behaviors, and introduce more accurate risk assessment metrics. This paper presents a system that can learn to predict the future braking behavior of a driver in a typically urban vehicle-pedestrian conflict, i.e., when a pedestrian enters a zebra crossing from the curb and a vehicle is approaching. The algorithm proposes a sequential prediction of relevant operational indicators that continuously describe the encounter process. A car driving simulator was used to collect reliable data on braking behaviours of a cohort of 68 licensed university students, who faced the same urban scenario. The vehicle speed, steering wheel angle, and pedal activity were recorded as the participants approached the crosswalk, along with the azimuth angle of the pedestrian and the relative longitudinal distance between the vehicle and the pedestrian: the proposed system employs the vehicle information as human driving decisions and the pedestrian information as explanatory variables of the environmental state. In fact, the pedestrian’s polar coordinates are usually calculated by an on-board millimeter-wave radar which is typically used to perceive the environment around a vehicle. All mentioned information is represented in the form of time series data and is used to train a recurrent neural network in a supervised machine learning process. The main purpose of this research is to define a system of behavioral profiles in non-collision conditions that could be used for enhancing the existing intelligent driving systems, e.g., to reduce the number of warnings when the driver is not on a collision course with a pedestrian. Preliminary experiments reveal the feasibility of the proposed system

Motion planning and tracking trajectory of an autonomous emergency braking pedestrian (AEB-P) system based on different brake pad friction coefficients on dry road surface

Accidents between vehicles and pedestrians usually occur when a pedestrian is crossing the road. An Autonomous Emergency Braking Pedestrian (AEB-P) is introduced to prevent collisions between vehicles and pedestrians. However, the performance of an AEB-P will be reduced when the brake pad is worn out on a dry road. In this study, the motion planning, namely Vehicle Conditional Artificial Potential Field (VC-APF), including a warning signal and emergency brake phase that generate the vehicle’s deceleration, is proposed to analyze the effect of brake pad on the AEB-P performance. Then, the vehicle’s deceleration is tracked by the tracking trajectory, where the PI controller is adapted to provide the optimum braking force. The function of PI control is to ensure the vehicle’s deceleration is approaching the desired deceleration. The performance of the proposed method has been simulated on the dry road surface with different brake pad coefficients; 0.4, 0.35, and 0.24. The simulation results show that the vehicle manages to stop colliding with a pedestrian on the dry road surface at the minimum safety distance range of 2.7-2.9 meters

Multi-level decision framework collision avoidance algorithm in emergency scenarios

With the rapid development of autonomous driving, the attention of academia has increasingly focused on the development of anti-collision systems in emergency scenarios, which have a crucial impact on driving safety. While numerous anti-collision strategies have emerged in recent years, most of them only consider steering or braking. The dynamic and complex nature of the driving environment presents a challenge to developing robust collision avoidance algorithms in emergency scenarios. To address the complex, dynamic obstacle scene and improve lateral maneuverability, this paper establishes a multi-level decision-making obstacle avoidance framework that employs the safe distance model and integrates emergency steering and emergency braking to complete the obstacle avoidance process. This approach helps avoid the high-risk situation of vehicle instability that can result from the separation of steering and braking actions. In the emergency steering algorithm, we define the collision hazard moment and propose a multi-constraint dynamic collision avoidance planning method that considers the driving area. Simulation results demonstrate that the decision-making collision avoidance logic can be applied to dynamic collision avoidance scenarios in complex traffic situations, effectively completing the obstacle avoidance task in emergency scenarios and improving the safety of autonomous driving

Research on Longitudinal Active Collision Avoidance of Autonomous Emergency Braking Pedestrian System (AEB-P)

The AEB-P (Autonomous Emergency Braking Pedestrian) system has the functional requirements of avoiding the pedestrian collision and ensuring the pedestrian’s life safety. By studying relevant theoretical systems, such as TTC (time to collision) and braking safety distance, an AEB-P warning model was established, and the traffic safety level and work area of the AEB-P warning system were defined. The upper-layer fuzzy neural network controller of the AEB-P system was designed, and the BP (backpropagation) neural network was trained by collected pedestrian longitudinal anti-collision braking operation data of experienced drivers. Also, the fuzzy neural network model was optimized by introducing the genetic algorithm. The lower-layer controller of the AEB-P system was designed based on the PID (proportional integral derivative controller) theory, which realizes the conversion of the expected speed reduction to the pressure of a vehicle braking pipeline. The relevant pedestrian test scenarios were set up based on the C-NCAP (China-new car assessment program) test standards. The CarSim and Simulink co-simulation model of the AEB-P system was established, and a multi-condition simulation analysis was performed. The results showed that the proposed control strategy was credible and reliable and could flexibly allocate early warning and braking time according to the change in actual working conditions, to reduce the occurrence of pedestrian collision accidents

ANALISI E MODELLAZIONE DELLE INTERAZIONI VEICOLO-PEDONE PER LO SVILUPPO DI SISTEMI ATTIVI DI ASSISTENZA ALLA GUIDA E DI PROTEZIONE DEI PEDONI

La sicurezza e la mobilità dei pedoni sono requisiti basilari che dovrebbero caratterizzare ogni sistema di trasporto urbano. Tuttavia, le morti degli utenti della strada più vulnerabili costituiscono ancora oggi una componente significativa di tutte le vittime della strada nel Mondo. Nonostante gli innumerevoli sforzi compiuti per l’innovazione tecnologica dei veicoli e il riesame degli spazi urbani, le statistiche sull’incidentalità dimostrano la necessità e l’importanza di sviluppare sempre più affidabili sistemi di protezione in grado di diminuire gli impatti sociali ed economici del sistema di trasporto. Sebbene sul mercato di massa siano stati immessi molti sistemi di frenata automatica di emergenza (o AEB, dall’inglese Automatic Emergency Braking), una misura di sicurezza chiave nei veicoli moderni in grado di evitare o mitigare gli effetti di una collisione, diversi ricercatori hanno individuato una nuova strategia per lo sviluppo efficiente di questi sistemi: migliorare la sicurezza dei pedoni nel traffico urbano richiede sistemi “intelligenti” in grado, non solo di comprendere lo stato attuale dell’interazione veicolo-pedone, ma di anticipare proattivamente il futuro modello di rischio dell’evento. In altre parole, prevedere in anticipo le decisioni degli utenti nella scena di traffico, interpretare i comportamenti dei conducenti e definire accurate metriche di valutazione del rischio sono gli obbiettivi da perseguire per raggiungere nuovi traguardi nell’ambito della mobilità sostenibile. Questo elaborato discute la natura globale del problema della sicurezza dei pedoni e i diversi approcci che sono stati sviluppati dai gruppi di ricerca nel Mondo per affrontarlo. Inoltre, la tesi presenta nel dettaglio lo studio, l’implementazione e l’analisi di un innovativo modello di valutazione del rischio, recentemente oggetto di pubblicazione su rivista internazionale, per l’efficientamento dei sistemi di assistenza alla guida esistenti. Il modello proposto, basato su moderne tecniche di Machine Learning e processi di analisi in linea con la letteratura scientifica più recente, è in grado di predire, fino a tre secondi nel futuro, il livello di rischio atteso negli incontri tra veicolo e pedone sulle strisce pedonali in funzione della rappresentazione attuale della scena di traffico tratta da radar e telecamere esterne al veicolo. Infatti, l’algoritmo prototipato fornisce una previsione sequenziale, su più orizzonti temporali, di indicatori di sicurezza operativi che descrivono in continuo il processo di incontro e permettono di annotare le interazioni conflittuali gravi. L’applicazione è stata ottimizzata attraverso dati di mobilità, acquisiti con un simulatore di guida avanzato ad elevato grado di realismo, su un campione di giovani conducenti. Questi ultimi hanno affrontato diversi conflitti veicolo-pedone su un percorso urbano virtuale pianificato. La conoscenza acquisita dal modello in questo contesto potrà essere sfruttata per facilitare l’adattamento online del sistema a nuove situazioni operative e alle diverse caratteristiche comportamentali degli utenti