Driver-like decision-making method for vehicle longitudinal autonomous driving based on deep reinforcement learning

Gao, Zhenhai; Yan, Xiangtong; Gao, Fei; He, Lei

doi:10.1177/09544070211063081

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…Two driving scenarios are extracted and reconstructed from the NGSIM dataset for validation and evaluation, i.e., an active lanechange scenario and an emergency braking scenario. The proposed MDU-CRA algorithm is compared to the probabilistic multi-modal expected trajectory prediction (PMETP) [35] trajectory predictor in terms of short-term motion prediction accuracy. The efficacy of our risk assessment model is gauged by benchmarking against a range of state-of-the-art models [36], [37] and employing multiple evaluation metrics.…”

Section: Performance Evaluationmentioning

confidence: 99%

Collision Risk Assessment for Intelligent Vehicles Considering Multi-Dimensional Uncertainties

Gao,

Bao,

Cui

et al. 2024

IEEE Access

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To ensure the reliability of autonomous driving, the system must be capable of potential hazard identification and appropriate response to prevent accidents. This involves the prediction of possible developments in traffic situations and an evaluation of the potential danger of future scenarios. Precise Collision Risk Assessment (CRA) faces complex challenges due to uncertainties inherent in vehicle and road environmental conditions. This paper introduces a new CRA approach, the Multi-Dimensional Uncertainties-CRA (MDU-CRA), which integrates uncertainties related to driver behavior, sensor perception, motion prediction models, and road infrastructure into a comprehensive risk evaluation framework. The estimation of vehicle state is initiated using Extended Kalman Filtering (EKF) to capture uncertainties in sensor perception. Concurrently, a probabilistic motion prediction model based on Gaussian distributions has been developed, which considers the uncertainty in driver behavior. Subsequently, the uncertainty of the road structure is modeled using a truncated Gaussian distribution. Finally, collision risk is quantified as the future probability of collision through heuristic Monte Carlo (MC) sampling. This paper presents the results of two experiments Firstly, our proposed method is demonstrated to outperform the reference neural network-based method in terms of short-term motion prediction accuracy. Secondly, two driving scenarios are extracted and reconstructed from the Next Generation Simulation (NGSIM) dataset for validation and evaluation, i.e., an active lane-change scenario and an emergency braking scenario. In the domain of collision risk assessment, our approach consistently outperforms other evaluation methods. It exhibits the capability to perceive collision risks 2 to 5 seconds in advance, significantly reducing the probability of imminent collision incidents.

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Section: Performance Evaluationmentioning

confidence: 99%

Collision Risk Assessment for Intelligent Vehicles Considering Multi-Dimensional Uncertainties

Gao,

Bao,

Cui

et al. 2024

IEEE Access

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“…The ultimate objective of autonomous driving is to realize human-like driving behavior [6,7]. In the vehicle moving process, the driver's task is to perceive the road environment, predict the intention of moving obstacles, determine driving behavior, and control vehicle direction and velocity to reach the expected goal [8].…”

Section: Introductionmentioning

confidence: 99%

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

Fu¹,

Fu²

2023

Computers, Materials &Amp; Continua

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This paper is to explore the problems of intelligent connected vehicles (ICVs) autonomous driving decision-making under a 5G-V2X structured road environment. Through literature review and interviews with autonomous driving practitioners, this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system. Secondly, situated on this framework, it builds a hierarchical finite state machine (HFSM) model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method (EWM) and analytic hierarchy process method (AHP) and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution (TOPSIS) and grey relational analysis (GRA). The HFSM model is composed of two layers: the global FSM model and the local FSM model. The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module, meanwhile pulsating feedback to the former as real-time refresh data. To identify different traffic scenarios in a cerebrum-like way, the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events, and the local FSM model is designed as 16 states and 8 characteristic events. In respect to designing a cerebrum-like algorithm for state transition, this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event; then, it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition. To verify the feasibility, reliability, and safety of the HFSM model as well as its TOPSIS-GRA state transition algorithm, this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform. The results display that the accuracy of obstacle detection gets 98%, lane line prediction is beyond 70 m, the speed of collision avoidance is higher than 45 km/h, the distance of collision avoidance is less than 5 m, path planning time for obstacle avoidance is averagely less than 50 ms, and brake deceleration is controlled under 6 m/s 2 . These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may 1052 CMC, 2023, vol.75, no.1 bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

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“…Currently, DRL has shown its advantages in vision and decision-making [ 32 , 33 ]. Some scholars have also tried to apply it to suspension control [ 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Deep Reinforcement Learning Control Algorithm for Active Suspension Considering Uncertain Time Delay

Wang,

Zhao

et al. 2023

Sensors

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The uncertain delay characteristic of actuators is a critical factor that affects the control effectiveness of the active suspension system. Therefore, it is crucial to develop a control algorithm that takes into account this uncertain delay in order to ensure stable control performance. This study presents a novel active suspension control algorithm based on deep reinforcement learning (DRL) that specifically addresses the issue of uncertain delay. In this approach, a twin-delayed deep deterministic policy gradient (TD3) algorithm with system delay is employed to obtain the optimal control policy by iteratively solving the dynamic model of the active suspension system, considering the delay. Furthermore, three different operating conditions were designed for simulation to evaluate the control performance: deterministic delay, semi-regular delay, and uncertain delay. The experimental results demonstrate that the proposed algorithm achieves excellent control performance under various operating conditions. Compared to passive suspension, the optimization of body vertical acceleration is improved by more than 30%, and the proposed algorithm effectively mitigates body vibration in the low frequency range. It consistently maintains a more than 30% improvement in ride comfort optimization even under the most severe operating conditions and at different speeds, demonstrating the algorithm’s potential for practical application.

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Driver-like decision-making method for vehicle longitudinal autonomous driving based on deep reinforcement learning

Cited by 8 publications

References 27 publications

Collision Risk Assessment for Intelligent Vehicles Considering Multi-Dimensional Uncertainties

Collision Risk Assessment for Intelligent Vehicles Considering Multi-Dimensional Uncertainties

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

Research on Deep Reinforcement Learning Control Algorithm for Active Suspension Considering Uncertain Time Delay

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