Realistic and diverse simulation scenarios with reactive and feasible agent
behaviors can be used for validation and verification of self-driving system
performance without relying on expensive and time-consuming real-world testing.
Existing simulators rely on heuristic-based behavior models for background
vehicles, which cannot capture the complex interactive behaviors in real-world
scenarios. To bridge the gap between simulation and the real world, we propose
TrajGen, a two-stage trajectory generation framework, which can capture more
realistic behaviors directly from human demonstration. In particular, TrajGen
consists of the multi-modal trajectory prediction stage and the reinforcement
learning based trajectory modification stage. In the first stage, we propose a
novel auxiliary RouteLoss for the trajectory prediction model to generate
multi-modal diverse trajectories in the drivable area. In the second stage,
reinforcement learning is used to track the predicted trajectories while
avoiding collisions, which can improve the feasibility of generated
trajectories. In addition, we develop a data-driven simulator I-Sim that can be
used to train reinforcement learning models in parallel based on naturalistic
driving data. The vehicle model in I-Sim can guarantee that the generated
trajectories by TrajGen satisfy vehicle kinematic constraints. Finally, we give
comprehensive metrics to evaluate generated trajectories for simulation
scenarios, which shows that TrajGen outperforms either trajectory prediction or
inverse reinforcement learning in terms of fidelity, reactivity, feasibility,
and diversity.

本论文提出了一个用于自动驾驶性能验证的仿真器 TrajGen，该仿真器基于自然驾驶数据进行强化学习，可以直接从人类演示中捕捉更真实的行为，并生成可靠而多样的行动轨迹来评估仿真场景中的性能表现。

TrajGen: 为自动驾驶生成现实和多样化的轨迹，并包含反应性和可行性的代理行为

TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving

Traffic accident anticipation aims to accurately and promptly predict the
occurrence of a future accident from dashcam videos, which is vital for a
safety-guaranteed self-driving system. To encourage an early and accurate
decision, existing approaches typically focus on capturing the cues of spatial
and temporal context before a future accident occurs. However, their
decision-making lacks visual explanation and ignores the dynamic interaction
with the environment. In this paper, we propose Deep ReInforced accident
anticipation with Visual Explanation, named DRIVE. The method simulates both
the bottom-up and top-down visual attention mechanism in a dashcam observation
environment so that the decision from the proposed stochastic multi-task agent
can be visually explained by attentive regions. Moreover, the proposed dense
anticipation reward and sparse fixation reward are effective in training the
DRIVE model with our improved reinforcement learning algorithm. Experimental
results show that the DRIVE model achieves state-of-the-art performance on
multiple real-world traffic accident datasets. Code and pre-trained model are
available at https://www.rit.edu/actionlab/drive.

本文提出了一种基于深度强化的交通事故预测方法 DRIVE，通过模拟仪表盘观察环境中的视觉注意机制和使用奖励机制改进强化学习算法来提高决策能力，进而实现较高准确性的预测。

DRIVE: 基于深度强化学习的事故预测与视觉解释

DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation

Monocular 3D object detection is a challenging task in the self-driving and
computer vision community. As a common practice, most previous works use
manually annotated 3D box labels, where the annotating process is expensive. In
this paper, we find that the precisely and carefully annotated labels may be
unnecessary in monocular 3D detection, which is an interesting and
counterintuitive finding. Using rough labels that are randomly disturbed, the
detector can achieve very close accuracy compared to the one using the
ground-truth labels. We delve into this underlying mechanism and then
empirically find that: concerning the label accuracy, the 3D location part in
the label is preferred compared to other parts of labels. Motivated by the
conclusions above and considering the precise LiDAR 3D measurement, we propose
a simple and effective framework, dubbed LiDAR point cloud guided monocular 3D
object detection (LPCG). This framework is capable of either reducing the
annotation costs or considerably boosting the detection accuracy without
introducing extra annotation costs. Specifically, It generates pseudo labels
from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measurements in
3D space, such pseudo labels can replace manually annotated labels in the
training of monocular 3D detectors, since their 3D location information is
precise. LPCG can be applied into any monocular 3D detector to fully use
massive unlabeled data in a self-driving system. As a result, in KITTI
benchmark, we take the first place on both monocular 3D and BEV
(bird's-eye-view) detection with a significant margin. In Waymo benchmark, our
method using 10% labeled data achieves comparable accuracy to the baseline
detector using 100% labeled data. The codes are released at
this https URL

通过使用粗略标签和无标签雷达点云伪标签，LPCG 可以在自动驾驶系统中显著降低标注成本或显著提高检测准确性，在 KITTI 基准测试和 Waymo 基准测试中都取得了很好的效果。