Autonomous driving systems rely on precise trajectory prediction for safe and efficient motion planning. Despite considerable efforts to enhance prediction accuracy, inherent uncertainties persist due to data noise and incomplete observations. Many strategies entail formalizing prediction outcomes into distributions and utilizing variance to represent uncertainty. However, our experimental investigation reveals that existing trajectory prediction models yield unreliable uncertainty estimates, necessitating additional customized calibration processes. On the other hand, directly applying current calibration techniques to prediction outputs may yield suboptimal results due to using a universal scaler for all predictions and neglecting informative data cues. In this paper, we propose Customized Calibration Temperature with Regularizer (CCTR), a generic framework that calibrates the output distribution. Specifically, CCTR 1) employs a calibration-based regularizer to align output variance with the discrepancy between prediction and ground truth and 2) generates a tailor-made temperature scaler for each prediction using a post-processing network guided by context and historical information. Extensive evaluation involving multiple prediction and planning methods demonstrates the superiority of CCTR over existing calibration algorithms and uncertainty-aware methods, with significant improvements of 11%-22% in calibration quality and 17%-46% in motion planning.

Driver Behavior Modeling (DBM) aims to predict and model human driving behaviors, which is typically incorporated into the Advanced Driver Assistance System to enhance transportation safety and improve driving experience. Inverse reinforcement learning (IRL) is a prevailing DBM technique with the goal of modeling the driving policy by recovering an unknown internal reward function from human driver demonstrations. However, the latest IRL-based design is inefficient due to the laborious manual feature engineering processes. Besides, the reward function usually experiences increased prediction errors when deployed for unseen vehicles. In this paper, we propose a novel deep learning-based reward function for IRL-based DBM with efficient model personalization via machine unlearning. We evaluate our approach on a highway simulation constructed using the realistic human driving dataset NGSIM. We deploy our approach on both a server GPU and an embedded GPU. The evaluation results show that our approach achieves a higher prediction accuracy compared with the latest IRL-based DBM approach that uses a weighted sum of trajectory features as the reward function. Our model personalization method obtains the highest accuracy and lowest latency compared with the baselines.

Indoor self-localization has become a highly desirable system function for smartphones. The existing systems based on imaging, radio frequency, and geomagnetic sensing may have sub-optimal performance when their limiting factors prevail. In this paper, we present a new indoor simultaneous localization and mapping (SLAM) system that is based on the smartphone&#x0027;s built-in audio hardware and inertial measurement unit (IMU). Our system uses a smartphone&#x0027;s loudspeaker to emit near-inaudible chirps and then the microphone to record the acoustic echoes from the indoor environment. The echoes contain the smartphone&#x0027;s location information with sub-meter granularity. To enable SLAM, we apply contrastive learning to train an echoic location feature (ELF) extractor, such that the loop closures on the smartphone&#x0027;s trajectory can be accurately detected from the associated ELF trace. The detection results effectively regulate the IMU-based trajectory reconstruction. The reconstructed trajectories are used for <italic>trajectory map superimposition</italic> and <italic>room geometry reconstruction</italic>. Extensive experiments show that our SLAM achieves median localization errors of <inline-formula><tex-math notation="LaTeX">$\text{0.1}\,\text{m}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{0.53}\,\text{m}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$\text{0.4}\,\text{m}$</tex-math></inline-formula> in a living room, an office, and a shopping mall, and outperforms both the Wi-Fi and geomagnetic SLAM systems. The room geometry reconstruction achieves up to 4&#x00D7; lower errors compared with the latest echo-based approaches.

Multi-modal fusion has shown initial promising results for object detection of autonomous driving perception. However, many existing fusion schemes do not consider the quality of each fusion input and may suffer from adverse conditions on one or more sensors. While predictive uncertainty has been applied to characterize single-modal object detection performance at run time, incorporating uncertainties into the multi-modal fusion still lacks effective solutions due primarily to the uncertainty's cross-modal incomparability and distinct sensitivities to various adverse conditions. To fill this gap, this paper proposes Uncertainty-Encoded Mixture-of-Experts (UMoE) that explicitly incorporates single-modal uncertainties into LiDAR-camera fusion. UMoE uses individual expert network to process each sensor's detection result together with encoded uncertainty. Then, the expert networks' outputs are analyzed by a gating network to determine the fusion weights. The proposed UMoE module can be integrated into any proposal fusion pipeline. Evaluation shows that UMoE achieves a maximum of 10.67%, 3.17%, and 5.40% performance gain compared with the state-of-the-art proposal-level multi-modal object detectors under extreme weather, adversarial, and blinding attack scenarios.