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Platoon formation of highway vehicles has the potential to significantly enhance road safety, improve highway utility, and increase traffic efficiency. However, various uncertainties and disturbances that are present in real-world driving conditions make the implementation of vehicular platoon a challenging problem. This study presents an H-infinity control method for a platoon of heterogeneous vehicles with uncertain vehicle dynamics and uniform communication delay. The requirements of string stability, robustness and tracking performance are systematically measured by the Hinfinity norm, and explicitly satisfied by casting into the linear fractional transformation format. A delay-dependent linear matrix inequality is derived to numerically solve the distributed controllers for each vehicle. The performances of the controlled platoon are theoretically analysed by using a delay-dependent Lyapunov function which includes a linear quadratic function of states during the delay period. Simulations with a platoon of heterogeneous vehicles are conducted to demonstrate the effectiveness of the proposed method under random parameters and external disturbances.

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Despite development efforts toward autonomous vehicle technologies, the number of collisions and driver interventions of autonomous vehicles tested in California seems to be reaching a plateau. One of the main reasons for this is the lack of defensive driving functionality; i.e. emergency collision avoidance when other human drivers make mistakes. In this paper, a collision avoidance/mitigation system (CAMS) is proposed to rapidly evaluate risks associated with all surrounding vehicles and to maneuver the vehicle into a safer region when faced with critically dangerous situations. First, a risk assessment module, namely, predictive occupancy map (POM), is proposed to compute potential risks associated with surrounding vehicles based on relative position, velocity, and acceleration. Then, the safest trajectory with the lowest risk level is selected among the 12 local trajectories through the POM. To ensure stable and successful collision avoidance of the ego-vehicle, the lateral and longitudinal acceleration profiles are planned while considering the vehicle stability limit. The performance of the proposed algorithm is validated based on side and rear-end collision scenarios, which are difficult to predict and to monitor. The simulation results show that the proposed CAMS via POM detect a collision risk 1.4 s before the crash, and avoids the collision successfully. INDEX TERMS Autonomous vehicle, advanced driver assist system (ADAS), collision avoidance, risk assessment, motion planning.

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