Accurate trajectory prediction of surrounding vehicles enables lower risk path planning in advance for autonomous vehicles, thus promising the safety of automated driving. A low-risk and high-efficiency path planning approach is proposed for autonomous driving based on the high-performance and practical trajectory prediction method. A long short-term memory (LSTM) network is trained and tested using the highD dataset, and the validated LSTM is used to predict the trajectories of surrounding vehicles combining the information extracted from vehicle-to-vehicle (V2V) technology. A risk assessment and mitigation-based local path planning algorithm is proposed according to the information of predicted trajectories of surrounding vehicles. Two driving scenarios are extracted and reconstructed from the highD dataset for validation and evaluation, i.e., an active lane-change scenario and a longitudinal collision-avoidance scenario. The results illustrate that the risk is mitigated and the driving efficiency is improved with the proposed path planning algorithm comparing to the constant-velocity prediction and the prediction method of the nonlinear input–output (NIO) network, especially when the velocity and trajectory with sudden changes. Note to Practitioners—This article was motivated by the problem of promising the safety decision-making and path planning through accurate environment prediction. There are two main parts included in this article. First, this article proposed one pragmatic approach to predict the environment movement correctly based on the long short-term memory (LSTM) approach. The prediction performance of LSTM was compared with nonlinear input–output (NIO). The results showed that the LSTM approach has a significant advantage in motivation prediction of the surrounded vehicles during path planning. The second part of this article is to make the decision and realize local path planning based on the risk assessment. The potential field-based approach is implemented on the risk assessment based on these accurate predictions. Some primary results demonstrate that the decision-making algorithm performs better under the accurate prediction model. The results also show that the safety and driving efficiency of the ego vehicle were improved by tracking the trajectory, which was planned based on the risk assessment. The only concern for the real-time application is the computation time; in future, we will figure it out how to further reduce the computation time.

An adaptive potential field is designed to adapt the acceleration/deceleration and mass of the obstacle. The potential fields are established in a transformed road coordinate system to improve the feasibility and robustness. A path planning method is proposed based on the designed adaptive potential field to improve the driving safety and the ride comfort of autonomous vehicles in complex driving scenarios, which including the cut-in, emergency braking, obstacle sudden accelerating during overtaking and the curve road driving scenarios. The effectiveness of the proposed method is validated by simulations with constructed and real data, respectively. The TTCs (Time-to-Collision) and the maximum lateral accelerations are used to evaluate the improvements on safety and ride comfort. The results show that both the driving safety and ride comfort are efficiently improved by using the proposed approach in emergency braking and accelerating scenarios. Meanwhile, the proposed method can be well applied in a curve road driving environment.

The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving.

This paper presents a novel interactive motion planning approach for left-turning of autonomous vehicles at uncontrolled intersections. A typical left-turning scenario that consists of agent vehicles and an autonomous ego vehicle is established. The autonomous ego vehicle aims to cross the uncontrolled intersection quickly and safely when meeting with the agent vehicles. A modified obstacle reciprocal collision avoidance (MORCA) prediction model is proposed to predict the trajectory of the agent vehicles with the considerations of longitudinal and lateral interaction-aware behaviors. The parameters of the MORCA prediction model are optimized using particle swarm optimization (PSO) with the inD datasets. Based on MORCA, a partially observable Markov decision process (POMDP) framework is established. The action and state spaces of the POMDP are extended reasonably to satisfy the requirement of real-time application. The simulation results show that the prediction precision is improved by 15% using the MORCA compared with the obstacle reciprocal collision avoidance (ORCA) prediction model. Meanwhile, the results manifest that the proposed MORCA-based POMDP planning method improves 43.8% in commuting efficiency compared with a traditional rule-based method, nearly as good as the vehicle-to-vehicle (V2V) communication is equipped. Note to Practitioners —The motivation of this article is to establish a safe and efficient planning framework for the left-turning of autonomous vehicles at uncontrolled intersections. Rule-based approaches were widely used in the previous studies but they are not optimal. In this paper, the lateral movements of vehicles at the intersection are considered for the first time. A MORCA prediction model is established and optimized to formulate a POMDP planning framework. The CARLA simulation demonstrates the effectiveness of the proposed MORCA-based POMDP planner. In future work, we will migrate the algorithm to a real-world test platform for experimentation.

Decision-making of connected and automated vehicles (CAV) includes a sequence of driving maneuvers that improve safety and efficiency, characterized by complex scenarios, strong uncertainty, and high real-time requirements. Deep reinforcement learning (DRL) exhibits excellent capability of real-time decision-making and adaptability to complex scenarios, and generalization abilities. However, it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs. This paper proposes a Mixture of Expert method (MoE) based on Soft Actor-Critic (SAC), where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state. To further enhance the performance of the DRL expert, a buffer zone is introduced in the reward function, preemptively applying penalties before insecure situations occur. In order to minimize collision and off-road rates, the Intelligent Driver Model (IDM) and Minimizing Overall Braking Induced by Lane changes (MOBIL) strategy are designed by heuristic experts. Finally, tested in typical simulation scenarios, MOE shows a 13.75% improvement in driving efficiency compared with the traditional DRL method with continuous action space. It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.

Eco-driving for Fuel Cell Hybrid Electric Vehicles (FCHEVs) through signalized intersections is a coupled problem of speed planning and powertrain control under complex environmental constraints. For global optimality and fast computation, this paper proposes a spatially convex long-term eco-driving approach for FCHEVs on signalized corridors. Considering road slopes, speed limits, and traffic lights, the original ecodriving problem is reformulated as a convex second-order cone programming problem by pre-optimization of the best green light window and convex approximation and convex relaxation of the hybrid powertrain. A powertrain-aware green window planner is first used to determine the optimal passing time windows through signalized intersections. Then the convex eco-driving problem is formulated and finally solved by concurrent optimization and sequential optimization according to whether the speed planning problem and energy management problem are coupled. Results show that the proposed concurrent convex optimization algorithm performs better fuel economy than the sequential optimization algorithm with similar computational time and can reduce motor energy consumption by 4.25% compared to an analytical speed planner. Compared to dynamic programming-based concurrent optimization, the proposed eco-driving method achieves 93.45% fuel economy with only 0.80% computational time