Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model

Background Road collisions and casualties pose a serious threat to commuters around the globe. Autonomous Vehicles (AVs) aim to make the use of technology to reduce the road accidents. However, the most of research work in the context of collision avoidance has been performed to address, separately, the rear end, front end and lateral collisions in less congested and with high inter-vehicular distances. Purpose The goal of this paper is to introduce the concept of a social agent, which interact with other AVs in social manners like humans are social having the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. The proposed social agent is based on a human-brain inspired mentalizing and mirroring capabilities and has been modelled for collision detection and avoidance under congested urban road traffic. Method We designed our social agent having the capabilities of mentalizing and mirroring and for this purpose we utilized Exploratory Agent Based Modeling (EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by Niazi and Hussain. Results Our simulation and practical experiments reveal that by embedding Richardson's arms race model within AVs, collisions can be avoided while travelling on congested urban roads in a flock like topologies. The performance of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure

Adaptable Emergency Braking Based on a Fuzzy Controller and a Predictive Model

This work presents the implementation of an adaptable emergency braking system for low speed collision avoidance, based on a frontal laser scanner for static obstacle detection, using a D-GPS system for positioning. A fuzzy logic decision process performs a criticality assessment that triggers the emergency braking system and modulates its behavior. This criticality is evaluated through the use of a predictive model based on a kinematic estimation, which modulates the decision to brake. Additionally a critical study is conducted in order to provide a benchmark for comparison, and evaluate the limits of the predictive model. The braking decision is based on a parameterizable braking model tuned for the target vehicle, that takes into account factors such as reaction time, distance to obstacles, vehicle velocity and maximum deceleration. Once activated, braking force is adapted to reduce vehicle occupants discomfort while ensuring safety throughout the process. The system was implemented on a real vehicle and proper operation is validated through extensive testing carried out at Tecnalia facilities.This project has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 692480. This Joint Undertaking receives support from the European Unions Horizon 2020 research and innovation programme and Germany, Netherlands, Spain, Austria, Belgium, Slovakia

Development of rear-end collision avoidance in automobiles

The goal of this work is to develop a Rear-End Collision Avoidance System for automobiles. In order to develop the Rear-end Collision Avoidance System, it is stated that the most important difference from the old practice is the fact that new design approach attempts to completely avoid collision instead of minimizing the damage by over-designing cars. Rear-end collisions are the third highest cause of multiple vehicle fatalities in the U.S. Their cause seems to be a result of poor driver awareness and communication. For example, car brake lights illuminate exactly the same whether the car is slowing, stopping or the driver is simply resting his foot on the pedal. In the development of Rear-End Collision Avoidance System (RECAS), a thorough review of hardware, software, driver/human factors, and current rear-end collision avoidance systems are included. Key sensor technologies are identified and reviewed in an attempt to ease the design effort. The characteristics and capabilities of alternative and emerging sensor technologies are also described and their performance compared. In designing a RECAS the first component is to monitor the distance and speed of the car ahead. If an unsafe condition is detected a warning is issued and the vehicle is decelerated (if necessary). The second component in the design effort utilizes the illumination of independent segments of brake lights corresponding to the stopping condition of the car. This communicates the stopping intensity to the following driver. The RECAS is designed the using the LabVIEW software. The simulation is designed to meet several criteria: System warnings should result in a minimum load on driver attention, and the system should also perform well in a variety of driving conditions. In order to illustrate and test the proposed RECAS methods, a Java program has been developed. This simulation animates a multi-car, multi-lane highway environment where car speeds are assigned randomly, and the proposed RECAS approaches demonstrate rear-end collision avoidance successfully. The Java simulation is an applet, which is easily accessible through the World Wide Web and also can be tested for different angles of the sensor

Vision-based active safety system for automatic stopping

ntelligent systems designed to reduce highway fatalities have been widely applied in the automotive sector in the last decade. Of all users of transport systems, pedestrians are the most vulnerable in crashes as they are unprotected. This paper deals with an autonomous intelligent emergency system designed to avoid collisions with pedestrians. The system consists of a fuzzy controller based on the time-to-collision estimate – obtained via a vision-based system – and the wheel-locking probability – obtained via the vehicle’s CAN bus – that generates a safe braking action. The system has been tested in a real car – a convertible Citroën C3 Pluriel – equipped with an automated electro-hydraulic braking system capable of working in parallel with the vehicle’s original braking circuit. The system is used as a last resort in the case that an unexpected pedestrian is in the lane and all the warnings have failed to produce a response from the driver

Intelligent automatic overtaking system using vision for vehicle detection

There is clear evidence that investment in intelligent transportation system technologies brings major social and economic benefits. Technological advances in the area of automatic systems in particular are becoming vital for the reduction of road deaths. We here describe our approach to automation of one the riskiest autonomous manœuvres involving vehicles – overtaking. The approach is based on a stereo vision system responsible for detecting any preceding vehicle and triggering the autonomous overtaking manœuvre. To this end, a fuzzy-logic based controller was developed to emulate how humans overtake. Its input is information from the vision system and from a positioning-based system consisting of a differential global positioning system (DGPS) and an inertial measurement unit (IMU). Its output is the generation of action on the vehicle’s actuators, i.e., the steering wheel and throttle and brake pedals. The system has been incorporated into a commercial Citroën car and tested on the private driving circuit at the facilities of our research center, CAR, with different preceding vehicles – a motorbike, car, and truck – with encouraging results

Traffic jam driving with NMV avoidance

n recent years, the development of advanced driver assistance systems (ADAS) – mainly based on lidar and cameras – has considerably improved the safety of driving in urban environments. These systems provide warning signals for the driver in the case that any unexpected traffic circumstance is detected. The next step is to develop systems capable not only of warning the driver but also of taking over control of the car to avoid a potential collision. In the present communication, a system capable of autonomously avoiding collisions in traffic jam situations is presented. First, a perception system was developed for urban situations—in which not only vehicles have to be considered, but also pedestrians and other non-motor-vehicles (NMV). It comprises a differential global positioning system (DGPS) and wireless communication for vehicle detection, and an ultrasound sensor for NMV detection. Then, the vehicle's actuators – brake and throttle pedals – were modified to permit autonomous control. Finally, a fuzzy logic controller was implemented capable of analyzing the information provided by the perception system and of sending control commands to the vehicle's actuators so as to avoid accidents. The feasibility of the integrated system was tested by mounting it in a commercial vehicle, with the results being encouraging