Autonomous Driving at Intersections: A Critical-Turning-Point Approach for Left Turns

Situation-Aware Left-Turning Connected and Automated Vehicle Operation at Signalized Intersections

One challenging aspect of the Connected and Automated Vehicle (CAV) operation in mixed traffic is the development of a situation-awareness module for CAVs. While operating on public roads, CAVs need to assess their surroundings, especially the intentions of non-CAVs. Generally, CAVs demonstrate a defensive driving behavior, and CAVs expect other non-autonomous entities on the road will follow the traffic rules or common driving behavior. However, the presence of aggressive human drivers in the surrounding environment, who may not follow traffic rules and behave abruptly, can lead to serious sa…

Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic

Multi-task intersection navigation including the unprotected turning left, turning right, and going straight in dense traffic is still a challenging task for autonomous driving. For the human driver, the negotiation skill with other interactive vehicles is the key to guarantee safety and efficiency. However, it is hard to balance the safety and efficiency of the autonomous vehicle for multi-task intersection navigation. In this paper, we formulate a multi-task safe reinforcement learning with social attention to improve the safety and efficiency when interacting with other traffic participants…

INTENT：一种用于交叉路口场景中车辆意图预测的LSTM框架及综合消融分析

INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis

研究针对交叉路口车辆意图预测问题，提出INTENT框架，运用LSTM模型提前2秒预测车辆意图动作，经在InD数据集上的实验和消融研究，取得99.71%准确率，可助力自动驾驶在复杂场景下安全行驶及增强轨迹预测。

Safe Deep Q-Network for Autonomous Vehicles at Unsignalized Intersection

We propose a safe DRL approach for autonomous vehicle (AV) navigation through crowds of pedestrians while making a left turn at an unsignalized intersection. Our method uses two long-short term memory (LSTM) models that are trained to generate the perceived state of the environment and the future trajectories of pedestrians given noisy observations of their movement. A future collision prediction algorithm based on the future trajectories of the ego vehicle and pedestrians is used to mask unsafe actions if the system predicts a collision. The performance of our approach is evaluated in two exp…

集成BEV-V2X感知、运动与占用融合预测及驾驶规划的复杂交通路口自动驾驶模型

An Autonomous Driving Model Integrated with BEV-V2X Perception, Fusion Prediction of Motion and Occupancy, and Driving Planning, in Complex Traffic Intersections

本文提出一种集成BEV-V2X感知、IMM-UKF融合预测与DRL决策规划的自动驾驶模型，在复杂路口仿真中实现了类人驾驶与多任务行为优化。

Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars

In autonomous driving, navigation through unsignaled intersections with many traffic participants moving around is a challenging task. To provide a solution to this problem, we propose a novel branched network G-CIL for the navigation policy learning. Specifically, we firstly represent such dynamic environments as graph-structured data and propose an effective strategy for edge definition to aggregate surrounding information for the ego-vehicle. Then graph convolutional neural networks are used as the perception module to capture global and geometric features from the environment. To generate …