Occlusion-aware On-road Autonomous Driving: A Path Planning Method in Combination with Honking Decision Making

Autonomous driving promises to be the main trend in the future intelligent transportation systems due to its potentiality for energy saving, and traffic and safety improvements. However, traditional autonomous vehicles’ behavioral decisions face consistency issues between behavioral decision and trajectory planning and shows a strong dependence on the human experience. In this paper, we present a planning-feature-based deep behavior decision method (PFBD) for autonomous driving in complex, dynamic traffic. We used a deep reinforcement learning (DRL) learning framework with the twin delayed deep deterministic policy gradient algorithm (TD3) to exploit the optimal policy. We took into account the features of topological routes in the decision making of autonomous vehicles, through which consistency between decision making and path planning layers can be guaranteed. Specifically, the features of a route extracted from path planning space are shared as the input states for the behavioral decision. The actor-network learns a near-optimal policy from the feasible and safe candidate emulated routes. Simulation tests on three typical scenarios have been performed to demonstrate the performance of the learning policy, including the comparison with a traditional rule-based expert algorithm and the comparison with the policy considering partial information of a contour. The results show that the proposed approach can achieve better decisions. Real-time test on an HQ3 (HongQi the third ) autonomous vehicle also validated the effectiveness of PFBD.

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Rollover-Free Path Planning for Off-Road Autonomous Driving

Electronics ◽

10.3390/electronics8060614 ◽

2019 ◽

Vol 8 (6) ◽

pp. 614

Author(s):

Xingyu Li ◽

Bo Tang ◽

John Ball ◽

Matthew Doude ◽

Daniel W. Carruth

Keyword(s):

Path Planning ◽

Free Path ◽

Autonomous Driving ◽

Planning Method ◽

Local Planning ◽

Planning Stage ◽

Local Path Planning ◽

Global Route ◽

Local Path ◽

Global Planning

Perception, planning, and control are three enabling technologies to achieve autonomy in autonomous driving. In particular, planning provides vehicles with a safe and collision-free path towards their destinations, accounting for vehicle dynamics, maneuvering capabilities in the presence of obstacles, traffic rules, and road boundaries. Existing path planning algorithms can be divided into two stages: global planning and local planning. In the global planning stage, global routes and the vehicle states are determined from a digital map and the localization system. In the local planning stage, a local path can be achieved based on a global route and surrounding information obtained from sensors such as cameras and LiDARs. In this paper, we present a new local path planning method, which incorporates a vehicle’s time-to-rollover model for off-road autonomous driving on different road profiles for a given predefined global route. The proposed local path planning algorithm uses a 3D occupancy grid and generates a series of 3D path candidates in the s-p coordinate system. The optimal path is then selected considering the total cost of safety, including obstacle avoidance, vehicle rollover prevention, and comfortability in terms of path smoothness and continuity with road unevenness. The simulation results demonstrate the effectiveness of the proposed path planning method for various types of roads, indicating its wide practical applications to off-road autonomous driving.

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Reinforcement Learning Path Planning Method with Error Estimation

Energies ◽

10.3390/en15010247 ◽

2021 ◽

Vol 15 (1) ◽

pp. 247

Author(s):

Feihu Zhang ◽

Can Wang ◽

Chensheng Cheng ◽

Dianyu Yang ◽

Guang Pan

Keyword(s):

Reinforcement Learning ◽

Path Planning ◽

Error Estimation ◽

Autonomous Driving ◽

Planning Method ◽

Robot Kinematics ◽

Learning Path ◽

Planning Algorithm ◽

Path Planning Algorithm ◽

Accumulated Error

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.

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