Methodology to assess safety effects of future Intelligent Transport Systems on railway level crossings

There is consistent evidence showing that driver behaviour contributes to crashes and near miss incidents at railway level crossings (RLXs). The development of emerging Vehicle-to-Vehicle and Vehicle-to-Infrastructure technologies is a highly promising approach to improve RLX safety. To date, research has not evaluated comprehensively the potential effects of such technologies on driving behaviour at RLXs. This paper presents an on-going research programme assessing the impacts of such new technologies on human factors and drivers’ situational awareness at RLX. Additionally, requirements for the design of such promising technologies and ways to display safety information to drivers were systematically reviewed. Finally, a methodology which comprehensively assesses the effects of in-vehicle and road-based interventions warning the driver of incoming trains at RLXs is discussed, with a focus on both benefits and potential negative behavioural adaptations. The methodology is designed for implementation in a driving simulator and covers compliance, control of the vehicle, distraction, mental workload and drivers’ acceptance. This study has the potential to provide a broad understanding of the effects of deploying new in-vehicle and road-based technologies at RLXs and hence inform policy makers on safety improvements planning for RLX

The influence of Vehicle Dynamics Control System on the Occupant’s Dynamic response during a Vehicle collision

This paper aims to apply a vehicle dynamics control system to mitigate a vehicle collision and to study the effects of this systems on the kinematic behaviour of the vehicle's occupant. A unique three-degree-of-freedom vehicle dynamics-crash mathematical model and a simplified lumped-mass occupant model are developed. The first model is used to define the vehicle body's crash parameters and it integrates a vehicle dynamics model with a model of the vehicle's front-end structure. In this model, the anti-lock braking system and the active suspension control system are co-simulated, and the associated equations of motion are developed. The second model aims to predict the effect of the vehicle dynamics control system on the kinematics of the occupant. The Lagrange equations are used to solve that model owing to the complexity of the obtained equations of motion. It is shown from the numerical simulations that the vehicle dynamics-crash response and occupant behaviour can be captured and analysed quickly and accurately. Furthermore, it is shown that the vehicle dynamics control system can affect the crash characteristics positively and that the occupant's behaviour is improved

Enhancement of Vehicle Safety and Improving Vehicle Yaw Behaviour Due to Offset Collisions Using Vehicle Dynamics

This study aims to optimise Vehicle Dynamic Control Systems (VDCS) in offset impact for vehicle collision mitigation. A proposed unique 3-D full-car mathematical model is developed and solved numerically to carry out this analysis. In this model, vehicle dynamics is studied together with the vehicle crash structural dynamics. Validation of the vehicle crash structure of the proposed model is achieved to ensure that the modelling of the crumple zone and the dynamic responses are reliable. It is demonstrated from the numerical simulations that the vehicle dynamic responses are captured and analysed and the influence of VDCS is determined accurately. In addition, it is shown that the mathematical model is flexible, useful and can be used in optimisation studies

Deep Predictive Models for Collision Risk Assessment in Autonomous Driving

In this paper, we investigate a predictive approach for collision risk assessment in autonomous and assisted driving. A deep predictive model is trained to anticipate imminent accidents from traditional video streams. In particular, the model learns to identify cues in RGB images that are predictive of hazardous upcoming situations. In contrast to previous work, our approach incorporates (a) temporal information during decision making, (b) multi-modal information about the environment, as well as the proprioceptive state and steering actions of the controlled vehicle, and (c) information about the uncertainty inherent to the task. To this end, we discuss Deep Predictive Models and present an implementation using a Bayesian Convolutional LSTM. Experiments in a simple simulation environment show that the approach can learn to predict impending accidents with reasonable accuracy, especially when multiple cameras are used as input sources.Comment: 8 pages, 4 figure

Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning

Recent advances in combining deep learning and Reinforcement Learning have shown a promising path for designing new control agents that can learn optimal policies for challenging control tasks. These new methods address the main limitations of conventional Reinforcement Learning methods such as customized feature engineering and small action/state space dimension requirements. In this paper, we leverage one of the state-of-the-art Reinforcement Learning methods, known as Trust Region Policy Optimization, to tackle intersection management for autonomous vehicles. We show that using this method, we can perform fine-grained acceleration control of autonomous vehicles in a grid street plan to achieve a global design objective.Comment: Accepted in IEEE Smart World Congress 201

Evaluation of a sudden brake warning system: Effect on the response time of the following driver

This study used a video-based braking simulation dual task to carry out a preliminary evaluation of the effect of a sudden brake warning system (SBWS) in a leading passenger vehicle on the response time of the following driver. The primary task required the participants (N = 25, 16 females, full NZ license holders) to respond to sudden braking manoeuvres of a lead vehicle during day and night driving, wet and dry conditions and in rural and urban traffic, while concurrently performing a secondary tracking task using a computer mouse. The SBWS in the lead vehicle consisted of g-force controlled activation of the rear hazard lights (the rear indicators flashed), in addition to the standard brake lights. Overall, the results revealed that responses to the braking manoeuvres of the leading vehicles when the hazard lights were activated by the warning system were 0.34 s (19%) faster compared to the standard brake lights. The SBWS was particularly effective when the simulated braking scenario of the leading vehicle did not require an immediate and abrupt braking response. Given this, the SBWS may also be beneficial for allowing smoother deceleration, thus reducing fuel consumption. These preliminary findings justify a larger, more ecologically valid laboratory evaluation which may lead to a naturalistic study in order to test this new technology in ‘real world’ braking situations

Making Transport Safer: V2V-Based Automated Emergency Braking System

An important goal in the field of intelligent transportation systems (ITS) is to provide driving aids aimed at preventing accidents and reducing the number of traffic victims. The commonest traffic accidents in urban areas are due to sudden braking that demands a very fast response on the part of drivers. Attempts to solve this problem have motivated many ITS advances including the detection of the intention of surrounding cars using lasers, radars or cameras. However, this might not be enough to increase safety when there is a danger of collision. Vehicle to vehicle communications are needed to ensure that the other intentions of cars are also available. The article describes the development of a controller to perform an emergency stop via an electro-hydraulic braking system employed on dry asphalt. An original V2V communication scheme based on WiFi cards has been used for broadcasting positioning information to other vehicles. The reliability of the scheme has been theoretically analyzed to estimate its performance when the number of vehicles involved is much higher. This controller has been incorporated into the AUTOPIA program control for automatic cars. The system has been implemented in Citroën C3 Pluriel, and various tests were performed to evaluate its operation

Metallic tube type energy absorbers: a synopsis

This paper presents an overview of energy absorbers in the form of tubes in which the material used is predominantly mild steel and/or aluminium. A brief summary is also made of frusta type energy absorbers. The common modes of deformation such as lateral and axial compression, indentation and inversion are reviewed. Theoretical, numerical and experimental methods which help to understand the behaviour of such devices under various loading conditions are outlined. Although other forms of energy absorbing materials and structures exist such as composites and honeycombs, this is deemed outside the scope of this review. However, a brief description will be given on these materials. It is hoped that this work will provide a useful platform for researchers and design engineers to gain a useful insight into the progress made over the last few decades in the field of tube type energy absorbers