Self-driving vehicles will soon be a reality, as main automotive companies have announced that they will sell their driving automation modes in the 2020s. This technology raises relevant controversies, especially with recent deadly accidents. Nevertheless, autonomous vehicles are still popular and attractive thanks to the improvement they represent to people’s way of life (safer and quicker transit, more accessible, comfortable, convenient, efficient, and environment-friendly). This paper presents a review of motion planning techniques over the last decade with a focus on highway planning. In the context of this article, motion planning denotes path generation and decision making. Highway situations limit the problem to high speed and small curvature roads, with specific driver rules, under a constrained environment framework. Lane change, obstacle avoidance, car following, and merging are the situations addressed in this paper. After a brief introduction to the context of autonomous ground vehicles, the detailed conditions for motion planning are described. The main algorithms in motion planning, their features, and their applications to highway driving are reviewed, along with current and future challenges and open issues.

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A Review of Motion Planning for Highway Autonomous Driving

A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.

Crash Mitigation in Motion Planning for Autonomous Vehicles

Improved grey wolf optimizer based on opposition and quasi learning approaches for optimization: case study autonomous vehicle including vision system

In this paper, we propose a progressive model predictive control scheme (PMPCS) by considering the cooperative control of local planning and path tracking for intelligent vehicles. An improved particle swarm optimization (IPSO) based model predictive control (MPC) method is developed to solve the planning and tracking problem. With the PMPCS, the total computational burden can be reduced sharply because of the seamless connection and mutual promotion between the optimization of two layers. Besides we also propose a novel planning algorithm, which can take traffic lights and overtaking time constraint into account. To solve these problems, we first combine model predictive control with artificial potential field (APF) to get a collision-free path by treating the time-varying safety constraints as the scope of the repulsive force and an asymmetrical lane potential field function. Furthermore, the pseudo velocity planning method is adopted to take traffic lights into account in the planning module. Simulation results show the reliability of the proposed algorithm and the advantages of the scheme compared with general hierarchical algorithm.

MPC-Based Cooperative Control Strategy of Path Planning and Trajectory Tracking for Intelligent Vehicles

Security problem is a fundamental issue for autonomous vehicles. Trajectory planning is a significant component of autonomous vehicle system, which directly influences the automated traffic safety. In this paper, the motion prediction of other traffic participants is considered. We use Monte Carlo simulation to predict the probabilistic occupancy of the object and give a map from probability statistics to actual scenarios. The non-time-based reference trajectory can be obtained by using high-definition map and lane detection. Then model predictive control is utilized to optimize the reference trajectory according to the current state of autonomous vehicle. Different prediction horizons and coordinate transformation are adopted to optimize the planning. By doing so, the constraint conditions can be easily involved and the result is more intuitive. The probabilistic occupancy of other traffic participants are computed offline and then the obtained results are used in real-time application. Therefore, the real-time computational burden is reduced. The crash probability is put forward to verify the feasibility of real-time trajectory in safety assessment module. Two typical scenarios are analyzed: lane change on the straight road and turning at the intersection. The simulation results illustrate the efficiency of our method.

Trajectory Planning and Safety Assessment of Autonomous Vehicles Based on Motion Prediction and Model Predictive Control

Path planning for autonomous vehicles in dynamic environments is an important but challenging problem, due to the constraints of vehicle dynamics and existence of surrounding vehicles. Typical trajectories of vehicles involve different modes of maneuvers, including lane keeping, lane change, ramp merging, and intersection crossing. There exist prior arts using the rule-based high-level decision making approaches to decide the mode switching. Instead of using explicit rules, we propose a unified path planning approach using Model Predictive Control (MPC), which automatically decides the mode of maneuvers. To ensure safety, we model surrounding vehicles as polygons and develop a type of constraints in MPC to enforce the collision avoidance between the ego vehicle and surrounding vehicles. To achieve comfortable and natural maneuvers, we include a lane-associated potential field in the objective function of the MPC. We have simulated the proposed method in different test scenarios and the results demonstrate the effectiveness of the proposed approach in automatically generating reasonable maneuvers while guaranteeing the safety of the autonomous vehicle.

Path planning for autonomous vehicles using model predictive control

In automated driving, the road geometry information such as waypoints is usually available from previously stored maps. In this paper, we present a scenario based Model Predictive Control (MPC) trajectory planning algorithm that consists of spatial planning with embedded temporal optimization that leverages waypoints information of the road. Our trajectory planning algorithm is structured such that the spatial and temporal planning is integrated so that both the longitudinal and lateral aspects, reflected in the shape and length of the planned trajectory, are dynamically changing to best negotiate the constraints from road curvature and surrounding vehicles. The concept of a vehicle connected to shadow trolleys traveling along the rails on the road is introduced. Given waypoints, a reference cubic spline can be constructed to define the rail for trolleys and form a curvilinear coordinate. This concept facilitates the description of vehicle motion, trajectories, and surroundings with respect to the trolley, which is especially convenient for a vehicle traveling on high curvature road and switchbacks. A temporal optimization of the trolley instances is first conducted, which in turn allows us to make a proper approximation and reduce an originally complex nonlinear spatial-temporal optimization problem into one that requires only Quadratic Programming (QP). We present simulation results of various challenging scenarios on complex road geometry with multiple surrounding vehicles of varying behavior to demonstrate the effectiveness and efficiency of the proposed algorithm. Simulation results show reasonable, flexible, and safe maneuvers.

Seungho Lee

Papers

Path planning for autonomous vehicles using model predictive control

Trajectory Planning with Shadow Trolleys for an Autonomous Vehicle on Bending Roads and Switchbacks