A path planning and tracking framework is presented to maintain a collision-free path for autonomous vehicles. For path-planning approaches, a 3-D virtual dangerous potential field is constructed as a superposition of trigonometric functions of the road and the exponential function of obstacles, which can generate a desired trajectory for collision avoidance when a vehicle collision with obstacles is likely to happen. Next, to track the planned trajectory for collision avoidance maneuvers, the path-tracking controller formulated the tracking task as a multiconstrained model predictive control (MMPC) problem and calculated the front steering angle to prevent the vehicle from colliding with a moving obstacle vehicle. Simulink and CarSim simulations are conducted in the case where moving obstacles exist. The simulation results show that the proposed path-planning approach is effective for many driving scenarios, and the MMPC-based path-tracking controller provides dynamic tracking performance and maintains good maneuverability.

Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2017.1291351

Policy and society related implications of automated driving: A review of literature and directions for future research

Significant effort has been made on designing user-acceptable driver assistance systems. To adapt to driver characteristics, this paper proposes a forward collision warning (FCW) algorithm that can adjust its warning thresholds in a real-time manner according to driver behavior changes, including both behavioral fluctuation and individual difference. This adaptive FCW algorithm overcomes the limit of traditional FCW with fixed risk evaluation models and fixed triggering thresholds by continuously monitoring driver braking behaviors in multiple lanes. A real-time identification algorithm for the warning thresholds is designed by using the recursive least squares method. Based on naturalistic experimental data, offline simulations show that this algorithm can match driver behavioral fluctuation and individual difference in long-time driving condition, and as time goes on, the adaptability to driver behavior is gradually improved, thus decreasing the false-alarm rate of FCW.

A Forward Collision Warning Algorithm With Adaptation to Driver Behaviors

https://hal.archives-ouvertes.fr/hal-01197212/document

Issues and challenges for pedestrian active safety systems based on real world accidents

In this paper we present a trajectory generation method for autonomous overtaking of static obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example, the autonomous car may have to move slightly into the opposite lane in order to cleanly see in front of a car ahead. Once it has gathered enough information about the road ahead, then the autonomous car can safely overtake. We generate safe trajectories by solving, in real-time, a non-linear constrained optimization, formulated as a Receding Horizon planner. The planner is guided by a high-level state machine, which determines when the overtake maneuver should begin. Our main contribution is a method that can maximize visibility, prioritizes safety and respects the boundaries of the road while executing the maneuver. We present experimental results in simulation with data collected during real driving.

Trajectory optimization for autonomous overtaking with visibility maximization

This study proposes an autonomous obstacle avoidance system not only by braking but also by steering, as one of the active safety technologies to prevent traffic accidents. The proposed system prevents the vehicle from colliding with a moving obstacle like a pedestrian jumping out from the roadside. In the proposed system, to avoid the predicted colliding position based on constant-velocity obstacle motion assumption, the avoidance trajectory is derived as connected two identical arcs. The system then controls the vehicle autonomously by the combined control of the braking and steering systems. In this paper, the proposed system is examined by real car experiments and its effectiveness is shown from the results of the experiments.

https://www.tandfonline.com/doi/pdf/10.1080/00423114.2012.672748

Juzo Isogai

Papers

Autonomous collision avoidance system by combined control of steering and braking using geometrically optimised vehicular trajectory