Entries by tsc_admin

Traffic modelling reveals CAV potential

Over the last five years, the world has seen a revolution in R&D towards the implementation of Connected and Autonomous Vehicles (CAVs). CAVs might be defined as vehicles without a human driver in the control loop, that may receive and transmit messages to other vehicles or roadside traffic management, allowing for better and more timely decisions. With such information and with direct control over the vehicles, it is hoped that significant strides can be made in the reduction of accidents and the increase of capacity.   The theoretical benefits are well known: the total elimination of accidents, and a potential 900% increase in capacity by using platooning systems. However, the practical benefits when interaction with non-CAV traffic is taken into consideration are likely to be far fewer. In order to accurately undertake meaningful cost-benefit assessments, simulation has become invaluable.   While the simulation of CAVs has been possible for many years, it has typically been undertaken through making a range of simple approximations and changes to pre-existing behaviour. These typically involve specifying a vehicle type and then associating it with certain new behaviours and parameters, such as shorter desired headways or faster reaction times, to mimic the removal of the human driver from the control loop.   Within HumanDrive however we are modelling autonomous vehicles using a more detailed approach. While the motion of such vehicles and how and when they accelerate or decelerate, and by how much, is a closely guarded trade secret of OEMs (Original Equipment Manufacturers) around the world, a number of useful base equations exist which give us a valid starting point. For example, earlier investigations, such as that by Milanes and Shladover (2014), investigated controlling a vehicle’s motion using the following equation:   uk = ukprev + kpek + kd?k Where, ukprev is the speed of the subject vehicle in the previous time step, and the gains kp and kd trying to adjust the time-gap error (ek) with respect to the preceding vehicle.   While the implementation of such systems in simulation is comparatively straightforward, there are a wide range of factors that will demonstrably affect these vehicles’ impact on the traffic system. For example, it is well known that these controllers are designed to eliminate overreaction of human drivers, making them ‘string stable’ and damping or even eliminating shockwaves. However, it is clear that this is a function of the number of vehicles and a critical density must be reached before these […]

CAV Cyber Security Framework

Vehicles have evolved to become increasingly connected with in-vehicle devices and external systems and infrastructure. Connected and Autonomous Vehicles (CAVs) will soon begin to appear on our roads whilst at the same time, cyber attacks across all sectors are increasing in regularity and sophistication. To provide assurance to the public that the security risks associated with CAV technology are being appropriately managed, SNC Lavalin’s Atkins business has developed a comprehensive CAV Cyber Security Framework (CCSF).   The CCSF is based on the five functions (Identify, Protect, Detect, Respond and Recover) of the globally recognised National Institute of Standards and Technology (NIST) Cyber Security Framework. The five functions of the CCSF are supplemented by a combination of industry standards and best practice to create a set of cyber security outcomes and objectives. This framework is being used by consortium partners to aid the delivery of a secure CAV ecosystem for HumanDrive.   For an overview of the CAV Cyber Security Framework see our infographic which you can also download here.