Surrounding Vehicles Motion Prediction for Risk Assessment and Motion Planning of Autonomous Vehicle in Highway Scenarios

Abstract

Safety is the cornerstone of autonomous driving vehicles. For autonomously controlled vehicles driving safely in complex and dynamic traffic scenarios, it is essential to predict the evolution of the current traffic situation in the near future and make an accurate situation risk assessment. The precise motion prediction of surrounding vehicles is an essential prerequisite for risk assessment and motion planning of autonomous vehicles. In this paper, we propose a risk assessment and motion planning method for autonomously controlled vehicles based on motion prediction of surrounding vehicles. Firstly, surrounding vehicles' trajectories are predicted based on fusing constant turn rate and acceleration-based motion prediction model and maneuver-based motion prediction model with interactive multiple models. Then, the collision risk assessment between autonomously controlled vehicle and surrounding vehicles is conducted with a collision risk index considering both the probability of collision event and collision severity. After that, the motion planning of autonomously controlled vehicle is formulated as a multiobjectives and multi-constraints optimization problem with model predictive control. Finally, the proposed method is applied to several traffic scenarios to validate its feasibility and effectiveness. INDEX TERMS Autonomous vehicles, motion prediction, risk assessment, motion planning, model predictive control.

Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

“…With the advent of vehicular social networks, busy traffic spots can be modeled as obstacles. Risk assessment for collision avoidance of nearby obstacles/vehicles will be desirable in future autonomous vehicle development [38]. A decision-making algorithm of risk assessment for collision avoidance was recently proposed for vehicles with different driving style preferences [39].…”

“…It should be noted that the proposed algorithm may be limited by the time needed to re-plan a safe path should an obstacle "suddenly" appear ahead. Care then has to be taken in risk assessment [38,39]. It should be noted that the proposed algorithm may be limited by the time needed to re-plan a safe path should an obstacle "suddenly" appear ahead.…”

“…It should be noted that the proposed algorithm may be limited by the time needed to re-plan a safe path should an obstacle "suddenly" appear ahead. Care then has to be taken in risk assessment [38,39].…”

Encoding Hidden Information onto Surfaces Using Polymerized Cholesteric Spherical Reflectors

“…While such developments can be highly beneficial, many concerns may be raised, among the most important being related to safety: as demonstrated by recurring tragic fatalities, autonomous vehicles have an insufficient understanding of their environment despite state‐of‐the‐art on‐board sensor and computation technology. [ 5–9 ] In restricted environments like research labs or power plants, reliable localization and object identification by robots are ensured by labeling surfaces with fiducial markers , [ 10 ] patterns of black squares on white background similar to the well‐known QR codes. Fiducial markers would be an enormous aid for robots deployed in human‐populated spaces, but black‐on‐white grid patterns are aesthetically and architecturally disruptive.…”

Linking physical objects to their digital twins via fiducial markers designed for invisibility to humans

“…Today's human-populated environments are largely off-limits for robots due to safety concerns, very much related to the technological challenges associated with making robots understand their surroundings [2][3][4][5]. There is good reason to address these challenges, because a future of a fully sensored and augmented environment to which our devices, including robots co-occupying our spaces, are linked in their knowledge of location, place, and identity, could provide many benefits to society, ranging from enhanced personal mobility to low ecological footprint technologies [6][7][8][9].…”