Effect of Packet Loss on a Connected Heavy Vehicle Collision Avoidance Algorithm Advanced Driver Assistance Systems in Heavy Commercial Road Vehicles are of increasing importance due to their high potential in reducing road accidents, where human error is the major contributor. A VehicletoVehicle communicationbased Collision Avoidance Algorithm (CAA) can help in reducing these accidents by complementing the onboard sensors and applying brakes automatically upon detecting a threat. However, due to the wireless nature and dynamic topology of this communication network, packet loss is a major challenge for safetyrelated applications like collision avoidance. Hence, the effect of packet loss on the performance of CAA was investigated using a HardwareinLoop experimental platform in this paper. It was observed that the desired final spacing was maintained but, early braking was observed when the host vehicle followed the lead vehicle at close distances.

Laboratory Emulator Using Connected Scaled Cars to Study Traffic Waves Traffic density has become one of the substantial problems in urban areas. Saturated roads increase the likelihood of vehicular collisions, mostly since the distance between the cars is decreased significantly. The emerging technology of communication between cars is suggested as an enabling technology to optimize traffic flow control. One solution to improve traffic flow on dense roads is to share kinematic properties among near cars. This could enable drivers or an automated system to get better decisions with respect to following the preceding cars. Control algorithms for cars driving in heavy traffic that considers the velocities, acceleration and spacing of the neighboring vehicles ahead, could eliminate traffic waves and reduce tail to head collisions. The purpose of this work is to design and construct a lab testbed based on scaled electric cars, which are remotely controlled by a PC, and to implement driving algorithms for these cars assuming the kinematic properties of the adjacent cars are known.